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K-nearest neighbors (KNN) algorithm is a common algorithm used for classification, and also a sub-routine in various complicated machine learning tasks. In this paper, we presented a quantum algorithm (QKNN) for implementing this algorithm based on the metric of Hamming distance. We put forward a quantum circuit for computing Hamming distance between testing sample and each feature vector in the training set. Taking advantage of this method, we realized a good analog for classical KNN algorithm by setting a distance threshold value t to select k − n e a r e s t neighbors. As a result, QKNN achieves O(n 3) performance which is only relevant to the dimension of feature vectors and high classification accuracy, outperforms Llyod’s algorithm (Lloyd et al. 2013) and Wiebe’s algorithm (Wiebe et al. 2014).

This study focuses on the improved initialization of initial centroids instead of random selection for the K-means algorithm. The random selection of initial seeds is a major drawback of the original Kmeans algorithm because it leads to less reliable result of clustering the data. The modified approach of the k-means algorithm integrates the computation of the weighted mean to improve the seeds initialization. This paper shows the comparison of K-Means and Modified K-Means algorithm, using the first simple dataset of four objects and the dataset for service vehicles. The two simple applications proved that the Modified K- Means of selecting initial centroids is more reliable than K-Means Algorithm. Clustering is better achieved in the modified k-means algorithm. Article DOI: http://dx.doi.org/10.20319/mijst.2016.22.4864 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

The original exact inference algorithm of the GP model runs very slow.We developed a recursive inference algorithm as an improvement.The new algorithm can obtain the same result in a shorter time.It works well for the real-time online prediction problems. Gaussian Process is a theoretically rigorous model for prediction problems. One of the deficiencies of this model is that its original exact inference algorithm is computationally intractable. Therefore, its applications are limited in the field of real-time online predictions. In this paper, a recursive prediction algorithm based on the Gaussian Process model is proposed. In recursive algorithms, the computational time of the next step can be greatly reduced by utilizing the intermediate results of the current step. The proposed recursive algorithm accelerates the prediction and avoids the loss of accuracy at the same time. Experiments are done on an ultra-short term electric load data set and the results are demonstrated to show the accuracy and efficiency of the new algorithm.

This paper proposed a normalised spline adaptive filtering algorithm to improve the stability of spline adaptive filtering (SAF) algorithm against the eigenvalue spread of the autocorrelation matrix of the input signal. The new adaptive filtering algorithm is based on the normalised least mean square (NLMS) approach and the value range of the learning rate in this algorithm is specified. This algorithm is called SAF-NLMS. In this work, first the derivation of the SAF-NLMS algorithm is given. Second, detailed convergence and the computational complexity analyses are carried out. Finally, the performance of the proposed algorithm is tested according to artificial datasets and real datasets. The achieved results present actually good performance. So, in practical engineering, the algorithm can be used to solve the problem of modeling or identification of nonlinear systems.

Objective: To improve the Edge Cover based Graph Coloring Algorithm (ECGCA) using independent set by incorporating parallel computing aspects in algorithm. Finding optimum time complexity is one of the main objectives of this paper. Methods/Statistical Analysis: This paper introduced some modification in ECGCA. Algorithm is implemented and tested using Java programming language. Java multithreading concept is used to achieve parallel computing in algorithm. DIMACS graph instances are used to test algorithm. Finding: Algorithm is tested on more than 75 DIMACS graph instances. To analyze the time complexity, execution time of algorithm in seconds is calculated by program. Algorithm is tested on different DIMACS graph instances. Test data is analyze in this paper and found that proposed algorithm executed in optimum time for large graphs. This paper also compared parallel algorithm and found that proposed parallel algorithm is less time complex than sequential algorithm. Most of the exact graph coloring algorithms are not suitable for large graph (more than 100 vertices) but proposed algorithm is tested on many large graphs and high execution success rate of algorithm is achieved. Application: This paper shows the experimental results of different type of application data. It means this algorithm can be used for maximum types of applications.

This paper considers the identification problem of multi-input-output-error autoregressive systems. A hierarchical gradient based iterative (H-GI) algorithm and a hierarchical least squares based iterative (H-LSI) algorithm are presented by using the hierarchical identification principle. A gradient based iterative (GI) algorithm and a least squares based iterative (LSI) algorithm are presented for comparison. The simulation results indicate that the H-LSI algorithm can obtain more accurate parameter estimates than the LSI algorithm, and the H-GI algorithm converges faster than the GI algorithm.

The junction tree algorithm is currently the most popular algorithm for exact inference on Bayesian networks. To improve the time complexity of the junction tree algorithm, we need to find a triangulation with the optimal total table size. For this purpose, Ottosen and Vomlel have proposed a depth-first search (DFS) algorithm. They also introduced several techniques to improve the DFS algorithm, including dynamic clique maintenance and coalescing map pruning. Nevertheless, the efficiency and scalability of that algorithm leave much room for improvement. First, the dynamic clique maintenance allows to recompute some cliques. Second, in the worst case, the DFS algorithm explores the search space of all elimination orders, which has size n!, where n is the number of variables in the Bayesian network. To mitigate these problems, we propose an extended depth-first search (EDFS) algorithm. The new EDFS algorithm introduces the following two techniques as improvements to the DFS algorithm: (1) a new dynamic clique maintenance algorithm that computes only those cliques that contain a new edge, and (2) a new pruning rule, called pivot clique pruning. The new dynamic clique maintenance algorithm explores a smaller search space and runs faster than the Ottosen and Vomlel approach. This improvement can decrease the overhead cost of the DFS algorithm, and the pivot clique pruning reduces the size of the search space by a factor of O ( n 2 ) . Our empirical results show that our proposed algorithm finds an optimal triangulation markedly faster than the state-of-the-art algorithm does. A state-of-the-art algorithm for optimal triangulation is proposed.Evaluated the degree of impact for employing the total table size as optimality criterion.A novel pruning rule for triangulation algorithm is proposed.A fast dynamic clique maintenance algorithm is proposed.

In this paper the hybrid and modified versions of the PSO algorithm applied to improvement of the search characteristics of the classical PSO algorithm in the development problem of the SVM classifier have been offered and investigated. A herewith two hybrid versions of the PSO algorithm assume the use of the classical Grid Search (GS) algorithm and the Design of Experiment (DOE) algorithm accordingly, and the modified version of the PSO algorithm realizes the simultaneous search of the kernel function type, the parameters values of the kernel function, and also the regularization parameter value. Besides, the questions of applicability of the k nearest neighbors (kNN) algorithm in the development problem of the SVM classifier have been considered.

A semi-real-time pattern-matching algorithm consisting of an offline and online stage is proposed. The approach of the proposed algorithm is to perform a significant amount of the calculation required by pattern matching in the offline stage. This necessitates only a small amount of calculation in the online process to reject a great number of mismatched positions. The proposed algorithm first uses triangle inequality and orthogonal decomposition to derive the lower bounds of the distances between the pattern and the candidate windows of the base image. Then, mismatched candidate windows are rejected if their lower bounds exceed an adaptive threshold. The proposed method accelerates the online processing effectively while yielding the identical result as a full search algorithm. The proposed algorithm was compared with other state-of-the-art algorithms and the result confirms that the proposed algorithm has a distinct speed advantage over the other algorithms for online processing.

This paper proposes a computationally efficient optimization algorithm for segmenting colour satellite images.CS algorithm incorporating Mantegna's and McCulloch's method for modeling levy flight is presented.PSO, DPSO, ABC and CS algorithms are compared with the proposed algorithm.All these optimization algorithms are exploited using three different objective functions.Performance assessment metrics demonstrated the improvement in the efficiency of the proposed algorithm. Satellite image segmentation is challenging due to the presence of weakly correlated and ambiguous multiple regions of interest. Several bio-inspired algorithms were developed to generate optimum threshold values for segmenting such images efficiently. Their exhaustive search nature makes them computationally expensive when extended to multilevel thresholding. In this paper, we propose a computationally efficient image segmentation algorithm, called CSMcCulloch, incorporating McCulloch's method for l e ? v y flight generation in Cuckoo Search (CS) algorithm. We have also investigated the impact of Mantegna's method for l e ? v y flight generation in CS algorithm (CSMantegna) by comparing it with the conventional CS algorithm which uses the simplified version of the same. CSMantegna algorithm resulted in improved segmentation quality with an expense of computational time. The performance of the proposed CSMcCulloch algorithm is compared with other bio-inspired algorithms such as Particle Swarm Optimization (PSO) algorithm, Darwinian Particle Swarm Optimization (DPSO) algorithm, Artificial Bee Colony (ABC) algorithm, Cuckoo Search (CS) algorithm and CSMantegna algorithm using Otsu's method, Kapur entropy and Tsallis entropy as objective functions. Experimental results were validated by measuring PSNR, MSE, FSIM and CPU running time for all the cases investigated. The proposed CSMcCulloch algorithm evolved to be most promising, and computationally efficient for segmenting satellite images. Convergence rate analysis also reveals that the proposed algorithm outperforms others in attaining stable global optimum thresholds. The experiments results encourages related researches in computer vision, remote sensing and image processing applications.

We develop a super-resolution algorithm using directional Huber-Markov regularization.We compare our algorithm with two other state-of-the-art algorithms.We perform quantitative evaluation using six performance metrics. A robust spatial-domain based super-resolution mosaicking algorithm is proposed. This technique incorporates a mosaicking algorithm, and a super-resolution reconstruction algorithm. The main contribution of this paper is the development of a super-resolution algorithm using a Huber Norm-based maximum likelihood (ML) estimation in combination with an adaptive directional Huber-Markov regularization. Another contribution is the development of a no-reference performance metric based on reciprocal singular value curve for quantitative evaluation of the proposed algorithm. Along with the above-mentioned metric, five other performance measurement metrics are used to assess the efficiency of the algorithm. The performance of this algorithm is compared with the performances of two different algorithms: the Tikhonov regularization-based and the total variation (TV)-based super-resolution mosaicking algorithms. Results show that the proposed algorithm outperforms the other two techniques in terms of lowest amount of blur and noise in the output.

The background, development, performance assessment, and analysis of a novel pattern recognition algorithm that is applicable to any set of binary images are discussed. The efficacy of the algorithm when applied to the problem of fingerprint recognition is quantified. The conclusion was that the algorithm is relatively poor as a fingerprint identification algorithm, averaging an equal error rate of approximately 19% as calculated by the rules specified in the Year 2000 Fingerprint Verification Competition. The positive attributes of the algorithm were its ultra-fast matching times, orientation independence, lack of rejection events, relative insensitivity to resolution difference, and one-way transformations. The mechanism of algorithm operation as applied to fingerprints was investigated using integral geometry. This investigation showed that the algorithm was an indirect measure of ridge width, which explained the algorithm’s relatively poor performance. Another set of experiments suggests that the algorithm may be well-suited to other pattern recognition problems, specifically cloud and precipitation particle recognition and camouflage recognition. In summary, the research extends the field of pattern recognition by developing, assessing the performance, and determining the mechanism of operation of a novel pattern recognition algorithm that is applicable to any set of binary images.

In this paper, we propose a distributed adaptive learning algorithm to train the coefficients of a widely linear autoregressive moving average model by measurements collected by the nodes of a network. We assume that each node uses the augmented complex adaptive infinite impulse response (ACA-IIR) filter as the learning rule, and nodes interact with each other under an incremental mode of cooperation. To derive the proposed algorithm, called the incremental ACAIIR (IACA-IIR), we firstly formulate the distributed adaptive learning problem as an unconstrained minimization problem. Then, we apply stochastic gradient optimization argument to solve it and derive the proposed algorithm. We further find the step size range where the stability of the proposed algorithm is guaranteed. We also introduce a reduced-complexity version of the IACA-IIR algorithm. Since the proposed algorithm relies on the augmented complex statistics, it can be used to model both types of complex-valued signals (proper and improper signals). To evaluate the performance of the proposed algorithm, we use both synthetic and real-world complex signals in our simulations. The results exhibit superior performance of the proposed algorithm over the non-cooperative ACA-IIR algorithm.

A* algorithm is widely used in the game for diameter, is currently one of the more popular heuristic search algorithm, but the algorithm has the problem of searching time and path. In this paper, a bidirectional search A* algorithm is proposed to improve the search efficiency and ensure the accuracy of the search, and effectively solve the problem of path twists and turns. Then, the algorithm is implemented by experiment simulation, which improves the effectiveness and feasibility of the algorithm in the large map.

Tree-structured compressive sensing (CS) shows that it is possible to recover tree-sparse signals using fewer measurements compared with conventional CS. However, performance guarantees rely heavily on the premise that an exact tree projection (ETP) algorithm is employed. Nevertheless, for a given sparsity, the condensing sort and select algorithm in the model-based compressive sampling matching pursuit (CoSaMP) algorithm can only yield an approximate tree projection. Therefore, in order to ensure reconstruction precision, the authors propose the combination of an ETP algorithm with the CoSaMP algorithm. Further, the hierarchical wavelet connected tree is also integrated into the ETP-CoSaMP algorithm to offset the high computational complexity of the ETP algorithm. Experimental results indicate that the hierarchical ETP based on CoSaMP algorithm (HETP-CoSaMP algorithm) enhances reconstruction accuracy while retaining reconstruction time that is comparable with that of the model-based CoSaMP algorithm.

Recently we developed a Bayesian-FBP (Filtered Backprojection) algorithm for CT image reconstruction. This algorithm is also referred to as the FBP-MAP (FBP Maximum a Posteriori) algorithm. This non-iterative Bayesian algorithm has been applied to real-time MRI, in which the k-space is under-sampled. This current paper investigates the possibility to extend this Bayesian-FBP algorithm by introducing more controlling parameters. Thus, our original Bayesian-FBP algorithm became a special case of the extended Bayesian-FBP algorithm. A cardiac patient data set is used in this paper to evaluate the extended Bayesian-FBP algorithm, and the result from a well-establish iterative algorithm with ${{\rm L}_1}$ -norms is used as the gold standard. If the parameters are selected properly, the extended Bayesian-FBP algorithm can outperform the original Bayesian-FBP algorithm.

This paper proposes a hybrid bat algorithm with natural-inspired algorithms for continuous optimization problem. In this study, the proposed algorithm combines the reproduction step from weed algorithm and genetic algorithm. The reproduction step is applied to clone each bat population by fitness values and the genetic algorithm is applied in order to expand the population. The algorithm is evaluated on eighteen benchmark problems. The computational results of the proposed algorithm are compared with the methods in the literature which are self-adaptive differential evolution (DE), traditional DE algorithm, intersection mutation differential evolution (IMDE) algorithm, and the JDE self-adaptive algorithm. Findings show that the algorithm produces several solutions obtained by the previously published methods especially for the continuous unimodal function, the quartic function, the multimodal function and the discontinuous step function. In addition, the finding shows that the proposed algorithm can produce optimal solutions efficiently on benchmark instances within short computational time.

Fuzzy rule-based classification systems have been used extensively in data mining. This paper proposes a fuzzy rule- based classification algorithm based on a quantum ant optimization algorithm. A method of generating the hierarchical rules with different granularity hybridization is used to generate the initial rule set. This method can obtain an original rule set with a smaller number of rules. The modified quantum ant optimization algorithm is used to generate the optimal individual. Compared to other similar algorithms, the algorithm proposed in this paper demonstrates higher classification accuracy and a higher convergence rate. The algorithm is proved to be convergent on theory. Some experiments have been conducted on the algorithm, and the results proved that the algorithm is feasible.

In risk assessment and predictive policing, biased data can yield biased results.

We introduce a fast Branch-and-Bound algorithm for optimal feature selection based on a U-curve assumption for the cost function. The U-curve assumption, which is based on the peaking phenomenon of the classification error, postulates that the cost over the chains of the Boolean lattice that represents the search space describes a U-shaped curve. The proposed algorithm is an improvement over the original algorithm for U-curve feature selection introduced recently. Extensive simulation experiments are carried out to assess the performance of the proposed algorithm (IUBB), comparing it to the original algorithm (UBB), as well as exhaustive search and Generalized Sequential Forward Search. The results show that the IUBB algorithm makes fewer evaluations and achieves better solutions under a fixed computational budget. We also show that the IUBB algorithm is robust with respect to violations of the U-curve assumption. We investigate the application of the IUBB algorithm in the design of imaging W -operators and in classification feature selection, using the average mean conditional entropy (MCE) as the cost function for the search.

In this paper, a self-learning genetic algorithm for solving combinatorial optimization problems is considered. The self-learning consists in changing the values of the probabilities of crossing and mutation in accordance with changing the value of the fitness function after operations in the next iteration of the algorithm. The results of comparing the proposed algorithm with the Holland algorithm by the problems of multiprocessor job scheduling and subset sum problem are presented.

This paper proposes a location algorithm that locates newly built p-facilities in the optimal area with minimum cost in a city of n districts. This problem has been classified as NP-hard, to which no polynomial time algorithm exists. The proposed algorithm improves the shortcomings of existing Myopic algorithm by constructing until p-facilities and exchanging locations of p-th facility for p=[1, n-1]. When applied to experimental data of n=5, 7, 10, 55, the proposed algorithm has obtained an approximate value nearest possible to the optimal solution take precedence of reverse-delete method. This algorithm is also simply executable using Excel.

Due to the characteristic that A* algorithm takes a long time when traversing an OPEN table and a CLOSED table, an improved method is proposed that is a new way of array storing in an OPEN table and a CLOSED table. Compared to the original A* algorithm, the way of array storing accesses the array elements by locating the number ranks each time you visit a specified element, which can be done by only one operation. The original A* algorithm requires the traverse of multiple nodes in order to find a specified element. The experimental results show that the comparison of the improved A* algorithm with the original A* algorithm shows that the operating efficiency is improved by more than 40%. Based on the improved A* algorithm the method preserves the advantages of the original A* algorithm, improving the operating efficiency of A* algorithm.

In this paper the authors propose an adaptive estimation algorithm for in-network processing of complex signals over distributed networks. In the proposed algorithm, as the incremental augmented complex least mean square (IAC-LMS) algorithm, nodes of the network are allowed to collaborate via incremental cooperation mode to exploit the spatial dimension; while at the same time are equipped with LMS learning rules to endow the network with adaptation. The authors have extracted closed-form expressions that show how IAC-LMS algorithm performs in the steady-state. The authors further have derived the required conditions for mean and mean-square stability of the proposed algorithm. The authors use both synthetic benchmarks and real world non-circular data to evaluate the performance of the proposed algorithm. Simulation results also reveal that the IAC-LMS algorithm is able to estimate both second order circular (proper) and non-circular (improper) signals. Moreover, IAC-LMS algorithm outperforms the non-cooperative solution.

According to the traditional stabilized biconjugate gradient algorithm (BiCGSTAB) deficiency in data locality, this paper proposed a QTBiCGSTAB algorithm whose core idea is that recursively divides sparse matrix with quarter tree into sub-matrix and reorders them, to improve the hit ratio of cache and enhance the algorithm’s efficiency. And the idea is good for algorithm being parallized, that is proved by the numerical experiments later. It mainly shows, firstly, QTBiCGSTAB algorithm is more efficiency than BiCGSTAB, and the speedup would reach 1:330. The target division length would be influnced on the algorithm’s performance; Secondly, for large linear system, parallized QTBiCGSTAB is more efficiency than serial’s.

A computationally simple and efficient algorithm for compressed sensing is proposed. The algorithm, a simple combination of the orthogonal projection algorithm and of a novel quasi-Newton optimization scheme, is termed Quasi-Newton Iterative Projection (QNIP). There are two main advantages of the proposed algorithm. First, the computation of the proposed algorithm is very simple, which involves the application of the sampling matrix and its transpose at each iteration. Second, the algorithm appears to require a fewer number of iterations for convergence, whilst it provides a higher rate of perfect recovery compared with the reference algorithms. The performance of the proposed algorithm is validated via theoretical analysis as well as some numerical examples.

The Map Maker algorithm which converts survey data into geometric data with 2-dimensional Cartesian coordinates has been previously published. Analysis of the performance of this algorithm is continuing. The algorithm is suitable for generating 2D maps and it would be helpful to have this algorithm generalized to generate 3D and higher dimensional coordinates. The trigonometric approach of the Map Maker algorithm does not extend well into higher dimensions however this paper reports on an algebraic approach which solves the problem. A similar algorithm called the Coordinatizator algorithm has been published which converts survey data defining a higher dimensional space of measured sites into the lowest dimensionalcoordinatization accurately fitting the data. Therefore the Coordinatizator algorithm is not a projection transformation whereas the n-dimensional Map Maker algorithm is.

Social network is a collection of heterogeneous multi-relational data represented by the graph, whose nodes represent object, whose edges represent relationships between nodes, and the weights represent the extent of the relationship between nodes. This paper gave a weighted K-means algorithm and introduced weighted K-means algorithm into social networks. Traditional k-means and most k-means variants are still computationally expensive for large datasets, however, the weighted K-means algorithm is to reduce the initial cluster centers blindness and randomness by eliminating noise point and narrowing the range of k values. Experiments datasets show that the weighted K-means algorithm significantly enhances the clustering quality. Therefore, the weighted K-means algorithm is effective and suitable for the social network. Algorithm’s error rate is smaller and accuracy is higher than that of traditional k-means algorithm.

We propose the novel data analysis algorithm which allows to identify exactly the position of a given point as exterior, interior, or boundary relatively to an intersection of the finite number of pattern sets. Due to the special structure of the problem under study, this algorithm can be realized not only by sequential, but also by parallel computing on the basis of appropriate model decomposition.

Interactive segmentation algorithm has been paid more and more attention in the field of image segmentation. Random walk algorithm is a proposed interactive segmentation algorithm based on graph theory, this paper proposes an improved Random Walk algorithm, the algorithm is more real-time than the original algorithm, and also has good robustness. This method adds the new improved Watershed algorithm to pre segmentation processing, reconstruct the connected domain graph, and the segmentation function Gauss area using 2 norm entropy reduction as a function of energy, finally it was solved by Random Walk algorithm. This method is faster than the original algorithm, the accuracy is satisfactory, and can be used for multi-target segmentation which has good robustness to noise.

The string matching problem is considered as one of the most interesting research areas in the computer science field because it can be applied in many essential different applications such as intrusion detection, search analysis, editors, internet search engines, information retrieval and computational biology. During the matching process two main factors are used to evaluate the performance of the string matching algorithm which are the total number of character comparisons and the total number of attempts. This study aims to produce an efficient hybrid exact string matching algorithm called Sinan Sameer Tuned Boyer Moore-Quick Skip Search (SSTBMQS) algorithm by blending the best features that were extracted from the two selected original algorithms which are Tuned Boyer-Moore and Quick-Skip Search. The SSTBMQS hybrid algorithm was tested on different benchmark datasets with different size and different pattern lengths. The sequential version of the proposed hybrid algorithm produces better results when compared with its original algorithms (TBM and Quick-Skip Search) and when compared with Maximum-Shift hybrid algorithm which is considered as one of the most recent hybrid algorithm. The proposed hybrid algorithm has less number of attempts and less number of character comparisons.

Nowadays many algorithms for mobile robot mapping in indoor environments have been created. In this work we use a Kinect 2.0 camera, a visible range cameras Beward B2720 and an infrared camera Flir Tau 2 for building 3D dense maps of indoor environments. We present the RGB-D Mapping and a new fusion algorithm combining visual features and depth information for matching images, aligning of 3D point clouds, a “loop-closure” detection, pose graph optimization to build global consistent 3D maps. Such 3D maps of environments have various applications in robot navigation, real-time tracking, non-cooperative remote surveillance, face recognition, semantic mapping. The performance and computational complexity of the proposed RGB-D Mapping algorithm in real indoor environments is presented and discussed.

Image matching plays an important role in computer vision. The features extracted by SIFT algorithm have high stability invariant to scale, rotation and light, so it is the most popular algorithm for image matching. While SIFT algorithm also has its disadvantages of high dimensional data and time-consuming. To solve this problem, the traditional method employs PCA algorithm to reduce dimensionality of the descriptors. While PCA is a linear dimensionality reduction algorithm which means that it can only be used for linear distributed data. This paper employs the fuzzy K-means algorithm to improve it (referred to as FKPCA) and improved RANSAC algorithm to eliminate false matching points after matching with PCA-SIFT and FKPCA-SIFT. From the experimental results, compared with PCA-SIFT algorithm, it can be seen that FKPCA-SIFT can keep the high matching accuracy for image. Moreover, FKPCA-SIFT can also be applied to non-linear data to expand the scope of PCA-SIFT and provides a better reference platform for further research.

The problem of local minimum cannot be avoided when it comes to nonlinear optimization in the learning algorithm of neural network parameters, and the larger the optimization space is, the more obvious the problem becomes. This paper proposes a new type of hybrid learning algorithm for three-layered feed-forward neural networks. This algorithm is based on three-layered feed-forward neural networks with output layer function, namely linear function, combining a quasi Newton algorithm with adaptive decoupled step and momentum (QNADSM) and iterative least square method to export. Simulation proves that this hybrid algorithm has strong self-adaptive capability, small calculation amount and fast convergence speed. It is an effective engineer practical algorithm.

The diamond search (DS) algorithm is one of the most efficient block matching motion estimation algorithms by far and has already been applied in MPEG2/4. Through our research, we found that there is still some redundancy in the algorithm. In this paper, an improved new difference based search (DBS) algorithm is proposed. Simulation results demonstrate that the new algorithm outperforms the well-known diamond search (DS) algorithm and four step-searches (4SS). It obtains almost the same Peak Signal to Noise Ratio (PSNR) while requires less computations than the DS algorithm and 4SS algorithm.

K-Means algorithm is a one of the most famous unsupervised clustering algorithm. It has many disadvantages, such as sensitivity to the initial clustering centers and computes all the data points multiple times when facing the increasing data volume. In order to overcome the above limitations, this paper proposes to make use of density idea to find k cluster centers by adjusting the threshold. Finally, we design and implementation of the K-Means algorithm on the modern Graphic Processing Unit (GPU). The ratio of distance between classes to distance within classes and speedup are used as evaluation criteria. The experiments indicate that the proposed algorithm significantly improves the stability and efficiency of K-Means algorithm.

Fuzzy c-means (FCM) algorithm is an unsupervised clustering algorithm for image segmentation, and has been widely applied because the segmentation results are consistent with human visual characteristics. Enhanced fuzzy c-means clustering (EnFCM) algorithm is the improved FCM algorithm, which reduces the computational complexity. But, both FCM algorithm and EnFCM algorithm, clustering number still need to be manually determined. This paper, in order to realize the automation degree of algorithm, presents an improved algorithm. It first analyzes the histogram, then automatically determines the clustering number and peak value of each class through use of the peak point detection technology, finally segments image by using EnFCM algorithm. Experiments show that this method is a kind of faster fuzzy clustering algorithm with automatic classification ability for image segmentation.

The optimization of the Electromagnetism-like Mechanism (EM) algorithm is analyzed, and a Hybrid Chaos Search (CS) Electromagnetism-like Mechanism Algorithm (CEM) is proposed in this paper. The new algorithm combines the advantages of both the CS method and the EM algorithm. To make the initial population more uniform, the CEM algorithm constructs it with the theory of good point set in number theory. The CS method, rather than the random linear search algorithm, is used in the new algorithm, which can effectively prevent the search process from terminating prematurely. Besides, an adaptive movement is used in the improved algorithm to speed up convergence, and a genetic coefficient is added to the update formula to update the locations of particles so that the particles are more likely to move into the other feasible regions. Experimental results show that the improved algorithm can converge to global optimums more effectively and accurately.

For improving the effect of multi-target tracking in dense target and clutter scenario, a centralized fuzzy optimal assignment algorithm (CMS-FOA) of three-sensor multi-target system is proposed. And on the base of this, a generalized probabilistic data association algorithm (CMS-FOAGPDA) based on CMS-FOA algorithm is presented. The fusion algorithm gets effective 3-tuple of measurement set by using components of several satisfactory solutions of the fuzzy optimal assignment problem and then uses generalized probabilistic data association algorithm to calculate the update states of targets. Simulation results show that, in the aspect of multi- target tracking accuracy, CMS-FOA algorithm is superior to the optimal assignment (CMS-OA) algorithm based on state estimate and CMS-FOAGPDA algorithm is better than CMS-FOA algorithm. But considering the time spent, CMS-FOA algorithm spends a minimum of time and CMS-FOAGPDA algorithm is exactly on the contrary. Therefore, compared with CMS-OA algorithm, the two algorithms presented in the study each has its advantages and should be chosen according to the needs of the actual application when in use.

In this work, we present a new distributed algorithm for a non-convex and nonsmooth dictionary learning problem. The proposed algorithm, named proximal primal-dual algorithm with increasing penalty (Prox-PDA-IP), is a primal-dual scheme, where the primal step minimizes certain approximation of the augmented Lagrangian of the problem, and the dual step performs an approximate dual ascent. We provide a proof outline for convergence to stationary points, which is mainly based on constructing a new potential function that is guaranteed to decrease after some finite number of iterations. Numerical results are presented to validate the effectiveness of the proposed algorithm.

The efficiency of the Wang-Mendel (WM) algorithm is severely affected by the number of fuzzy rules and data scale. Thus, this paper proposes a reduced weighted WM algorithm to solve the problem by balancing the completeness and the computation time. The clustering algorithm is first introduced to obtain the cluster centers. Then, only the cluster centers are used to generate fuzzy rules, namely, the most important fuzzy rules are obtained. Finally, the weighted average is used to improve the accuracy of the WM algorithm. The proposed algorithm can save much computation time and storage space. The results of the experiments demonstrate that the proposed algorithm has high efficiency with high precision.

K-means clustering algorithm is an influential algorithm in data mining. The traditional K-means algorithm has sensitivity to the initial cluster centers, leading to the result of clustering depends on the initial centers excessively. In order to overcome this shortcoming, this paper proposes an improved K-means text clustering algorithm by optimizing initial cluster centers. The algorithm first calculates the density of each data object in the data set, and then judge which data object is an isolated point. After removing all of isolated points, a set of data objects with high density is obtained. Afterwards, chooses k high density data objects as the initial cluster centers, where the distance between the data objects is the largest. The experimental results show that the improved K-means algorithm can improve the stability and accuracy of text clustering.

In paper the parallel algorithm of number comparing in residue number system of the special form (2n-1, 2n, 2n+1) based on use of diagonal functions from work [5] and a method of the recursive doubling from work [9] is offered. The offered algorithm allows scoring 31% in area of the occupied device and 18% in time delay in comparing with algorithm from work [9].

The improvement of pattern matching algorithm is a key to improve the efficiency of network intrusion detection. Firstly, this thesis analyzes the current mainstream of single mode and multi-mode matching algorithm; then an improved multi-mode matching KR-BM2 algorithm based on KR algorithm and BM2 algorithm is proposed. BM2 algorithm using dual characters determines to move distance of the pattern string to the right, and uses the hash algorithm to reduce the number of comparisons in each match. KR-BM2 algorithm realizes the multi-pattern matching algorithm and the matching performance is improved. Finally the improvement of the proposed algorithm in the time performance is verified by experimental results.