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Locally and globally asymptotical stability on equilibria of delayed neural networks with saturation activation functions are studied by the Razumikhin-type theorems, which are the main approaches to study the stability of functional differential equations, and some new stability conditions are obtained, which are constructed by the networks' parameters. In the case of local stability conditions, the attracted fields of equilibria are also estimated. All results obtained in this paper need only to compute the eigenvalues of some matrices or to verify some inequalities to be holden. [ABSTRACT FROM AUTHOR]

In this paper, we extend the Cohen-Grossberg neural networks from classical to fuzzy sets, and propose the fuzzy Cohen-Grossberg neural networks (FCGNN). The global exponential stability of FCGNN with time-varying delays is studied. Without assuming the boundedness and differentiability of the activation functions, based on the properties of M-matrix, by constructing vector Liapunov functions and applying differential inequalities, the sufficient conditions ensuring existence, uniqueness, and global exponential stability of the equilibrium point of fuzzy Cohen-Grossberg neural networks with variable delays are obtained. [ABSTRACT FROM AUTHOR]

This paper puts forward a multi-stages competitive neural networks approach for motion trajectory pattern analysis and learning. In this method, the rival penalized competitive learning method, which could well overcome the competitive networks' problems of the selection of output neurons number and weight initialization, is used to discover the distribution of the flow vectors according to the trajectories' time orders. The experiments on different sites with CCD and infrared cameras demonstrate that our method is valid for motion trajectory pattern learning and can be used for anomaly detection in outdoor scenes. [ABSTRACT FROM AUTHOR]

Multi-Constrained Shortest Path (MCSP) selection is a fundamental problem in communication networks. Since the MCSP problem is NP-hard, there have been many efforts to develop efficient approximation algorithms and heuristics. In this paper, a new algorithm is proposed based on vectorial Autowave-Competed Neural Network which has the characteristics of parallelism and simplicity. A nonlinear cost function is defined to measure the autowaves (i.e., paths). The M-paths limited scheme, which allows no more than M autowaves can survive each time in each neuron, is adopted to reduce the computational and space complexity. And the proportional selection scheme is also adopted so that the discarded autowaves can revive with certain probability with respect to their cost functions. Those treatments ensure in theory that the proposed algorithm can find an approximate optimal path subject to multiple constraints with arbitrary accuracy in polynomial-time. Comparing experiment results showed the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Due to the time-consuming calculation for the forward kinematics of a 3-PRS (prismatic-revolute-spherical) parallel manipulator, neither the kinematic nor dynamic control algorithm can be implemented on real time. To deal with such problem, the forward kinematics is solved by means of artificial neural network (NN) approach in this paper. Based on the trained NN, the kinematic control of the manipulator is carried out by resorting to an ordinary control algorithm. Simulation results illustrate that the NN can approximate the forward kinematics perfectly, which leads to ideal control results of the parallel manipulator. [ABSTRACT FROM AUTHOR]

Regression analysis between features of high-dimension is receiving attention in environmental learning of mobile robot. In this paper, we propose a novel framework, namely General regression neural network (GRNN), for approximating the functional relationship between high-dimensional map features and robot's states. We firstly adopt PCA to preprocess images taken from omnidirenctional vision. The method extracts map features optimally and reduces the correlated features while keeping the minimum reconstruction error. Then, the robot states and corresponding features of the training panoramic snapshots are used to train the given neural network. This enables robot to memorize the environmental features as well as to predict available scene given its location information. Experimental results are shown finally. [ABSTRACT FROM AUTHOR]

This paper investigates the use of SOM to process the signal of a 2D laser scanner encountered in feature extraction (corner) and mobile robot self-localization in indoor environments. It presents the method of combining SOM with occupancy grid matching to improve the self-localization performance at the lower computational cost. Experimental results demonstrate that this method can reliably extract the feature of corner point and can effectively improve the self-localization performance of mobile robot. [ABSTRACT FROM AUTHOR]

A robust adaptive control based on generalized dynamic fuzzy neural network (GD-FNN) is presented for robot manipulators. Fuzzy control rules can be generated or deleted automatically according to their significance to the control system, and no predefined fuzzy rules are required. Being use of radial basis function neural network (RBFNN) the learning speed is very fast. The asymptotic stability of the control system is established using Lyapunov theorem. Simulations are given for a two-link robot in the end of paper, and validated the control arithmetic. [ABSTRACT FROM AUTHOR]

This paper presents a novel omni-directional mobile robot with obstacle avoidance and self-location function. Since a special transmission mechanism is designed, we introduce the mechanism design of the mobile robot and analyze its kinematics. A fuzzy-neural control algorithm is presented to realize the obstacle avoidance of the mobile robot. The obstacle avoidance system based on FKCN and the control algorithm for obstacle avoidance are described. A self-localization technique that uses the ceiling light for the landmark is proposed. Using this self-location function, the mobile robot could locate itself in a world coordinate system. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm to detect the obstacles in outdoor unstructured environment with monocular vision. It makes use of motion cues in the video streams. Firstly, optical flow at feature points is calculated. Then rotation of the camera and FOE(focal of expansion) are evaluated separately. A non-linear optimization method is adopted to refine the rotation and FOE. Finally, we get inverse TTC(time to contact) with rotation and FOE and detect the obstacles in the scene. The algorithm doesn't need any assumption that the ground is flat or partially flat as the conventional methods. So it is suitable for outdoor unstructured environment. Qualitative and quantitative experiment results show that our algorithm works well on different kinds of terrains. [ABSTRACT FROM AUTHOR]

This paper proposes a novel attention selection system with competition neural network supervised by visual memory. As compared with others, this system can not only attend some salient regions randomly according to sensory information but also mainly focus on some learned objects by the visual memory. So it can be applied in robot self-localization or object tracking. The weights of neural networks can be adapted in real time to environment change. [ABSTRACT FROM AUTHOR]

Path planning is an important task in mobile robot control. When the robot must move rapidly from any arbitrary start positions to any target positions in environment, a proper path must avoid both static obstacles and moving obstacles of arbitrary shape. In this paper, an obstacle avoidance path planning approach for mobile robots is proposed by using Ant-Q algorithm. Ant-Q is an algorithm in the family of ant colony based methods that are distributed algorithms for combinatorial optimization problems based on the metaphor of ant colonies. In the simulation, we experimentally investigate the sensitivity of the Ant-Q algorithm to its three methods of delayed reinforcement updating and we compare it with the results obtained by other heuristic approaches based on genetic algorithm or traditional ant colony system. At last, we will show very good results obtained by applying Ant-Q to bigger problem: Ant-Q find very good path at higher convergence rate. [ABSTRACT FROM AUTHOR]

This paper focuses on seeking an appropriate number of rules for a T-S inference system. A growing and pruning strategy in neural network is employed, which relates one fuzzy rule's contribution to the modeling accuracy by a statistic criterion, such that fuzzy rules is added/removed, whereas all the parameters can learn using EKF, both absolutely on-line and with small computation. A simulation for nonlinear system identification illustrates the good performance. [ABSTRACT FROM AUTHOR]

Due to its structural simplicity and good properties, radial basis function (RBF) neural network has increasingly been used in many areas for the solution of difficult real-world problems, especially the nonlinear system dynamic modeling. However, the major problem toward using RBF network is the appropriate selection of radial basis function parameters. The basis function parameters are in general the centers and the widths. Our attention in this paper is focused on the configuring the optimal set of parameters to make the networks small and efficient based on rough set theory (RST), which is a valid mathematical tool to perform data reduction. RST is first applied to extract the underlying rules from data. The condition components of the rules are then mapped into network centers. For improve performance, the width parameter of each hidden neuron is initialized individual. The valid of this algorithm is illustrated by an example on the modeling of a ship synchronous generator. [ABSTRACT FROM AUTHOR]

Dynamic programming (DP) is a useful tool for solving many control problems, but for its complexity in computation, traditional DP control algorithms are not satisfactory in fact. So we must look for a new method which not only has the advantages of DP but also is easier in computation. In this paper, approximate dynamic programming (ADP) based controller system has been used to solve a ship heading angle keeping problem. The ADP controller comprises successive adaptations of two neural networks, namely action network and critic network which approximates the Bellman equations associated with DP. The Simulation results show that the ship keeps the desired heading satisfactorily. [ABSTRACT FROM AUTHOR]

Operon represents a basic organizational unit in microbial genomes. Operon prediction is an important step to study genic transcriptional and regulatory mechanism in microbial genomes. This paper predicted operons in the Escherichia coli K12 genome using neural network based on four types of genomic log-likelihood data. First this method estimated the log-likelihood values for intergenic distances, COG gene functions, conserved gene pairs and phylogenetic profiles, and then used these information by a generalized regression neural network to discriminate pairs of genes within operons (WO pairs) or transcription unit borders (TUB pairs). We test the method on E. coli K12 and find that it can obtain average sensitivity, specificity and accuracy at 85.9%, 89.2% and 87.9% respectively, which indicates that the proposed method has a powerful capability for operon prediction. [ABSTRACT FROM AUTHOR]

Estimating the non-flat function which comprises both the steep variations and the smooth variations is a hard problem. The existing kernel methods with a single common variance for all the regressors can not achieve satisfying results. In this paper, a novel multi-scale model is constructed to tackle the problem by orthogonal least squares regression (OLSR) with wavelet kernel. The scheme tunes the dilation and translation of each wavelet kernel regressor by incrementally minimizing the training mean square error using a guided random search algorithm. In order to prevent the possible over-fitting, a practical method to select termination threshold is used. The experimental results show that, for non-flat function estimation problem, OLSR outperforms traditional methods in terms of precision and sparseness. And OLSR with wavelet kernel has a faster convergence rate as compared to that with conventional Gaussian kernel. [ABSTRACT FROM AUTHOR]

This paper firstly provides a short introduction to least square support vector machine (LS-SVM), then provides sequential minimal optimization (SMO) based on Pruning Algorithms for LS-SVM, and uses LS-SVM to model nonlinear systems. Simulation experiments are performed and indicated that the proposed method provides satisfactory performance with excellent accuracy and generalization property and achieves superior performance to the conventional method based on common LS-SVM and neural networks. [ABSTRACT FROM AUTHOR]

A communication network is a highly complex nonlinear dynamical system. To avoid congestion collapse and keep network utilization high, many congestion control methods have been proposed. In this paper, a new framework, using Adaptive Critic Designs (ACD) based on Approximate Dynamic Programming (ADP) theory, is presented for network congestion control. At the present time, almost all ACD controllers are designed for centralized control system. In the new frame, the whole network is considered as a multiple non-cooperative ACDs control system, wherein, each source controller is governed by an ACD. This frame provides a new approach to solve the congestion control problem of the networks. [ABSTRACT FROM AUTHOR]

Normal fuzzy CMAC neural network performs well because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. In this paper, we use recurrent technique to overcome these problems and propose a new CMAC neural network, named recurrent fuzzy CMAC (RFCMAC). Since the structure of RFCMAC is more complex, normal training methods are difficult to be applied. A new simple algorithm with a time-varying learning rate is proposed to assure the learning algorithm is stable. [ABSTRACT FROM AUTHOR]

Manifold learning algorithms have been proven to be capable of discovering some nonlinear structures. However, it is hard for them to extend to test set directly. In this paper, a simple yet effective extension algorithm called PIE is proposed. Unlike LPP, which is linear in nature, our method is nonlinear. Besides, our method will never suffer from the singularity problem while LPP and KLPP will. Experimental results of data visualization and classification validate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

A novel online dynamic value system for machine learning is proposed in this paper. The proposed system has a dual network structure: data processing network (DPN) and information evaluation network (IEN). The DPN is responsible for numerical data processing, including input space transformation and online dynamic data fitting. The IEN evaluates results provided by DPN. A dynamic three-curve fitting (TCF) scheme provides statistical bounds to the curve fitting according to data distribution. The system uses a shift register communication channel. Application of the proposed value system to the financial analysis (bank prime loan rate prediction) is used to illustrate the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

This paper proposes novel hierarchical self-organizing associative memory architecture for machine learning. This memory architecture is characterized with sparse and local interconnections, self-organizing processing elements (PE), and probabilistic synaptic transmission. Each PE in the network dynamically estimates its output value from the observed input data distribution and remembers the statistical correlations between its inputs. Both feed forward and feedback signal propagation is used to transfer signals and make associations. Feed forward processing is used to discover relationships in the input patterns, while feedback processing is used to make associations and predict missing signal values. Classification and image recovery applications are used to demonstrate the effectiveness of the proposed memory for both hetero-associative and auto-associative learning. [ABSTRACT FROM AUTHOR]

The special scenario of the topology in the cellular Ad Hoc networks was analyzed and a novel cross layer power control game algorithm based on Neural Fuzzy Connection Admission Controller (NFCAC) was proposed in this paper. NFCAC has been successfully applied in the control-related problems of neural networks. However, there is no discussion about the power control game algorithm and location recognition based on NFCAC in cellular Ad Hoc networks. The proposed algorithm integrated the attributes both of NFCAC and the topology space in special scenario. The topology and the power consumption of each node can all be optimized due to the minimum link occupation with the help of the algorithm. Simulation results show that the novel algorithm can give more power control guarantee to cellular Ad Hoc networks in the variable node loads and transmitting powers, and make the node more stable to support multi-hops at the same time. [ABSTRACT FROM AUTHOR]

Reheat steam circle system is usually used in modern super-high parameters unit of power plant, which has the characteristics of long process channel, large inertia and long time lag, etc. Thus conventional PID control strategy cannot achieve good control performance. Prompted by the feedback regulation mechanism of biology immune response and the virtues of CMAC neural network, a composite control strategy based on CMAC neural network and immune PID controller is presented in this paper, which has the effect of feed-forward control for load changes as the unit load channel signal of reheat steam temperature is transmitted to the CMAC neural network to take charge of load change effects. The input signal of the controlled system are weighted and integrated by the output signals of CMAC neural network and immune PID controller, and then a variable parameter robust controller is constituted to act on the controlled system. Thus, good regulating performance is guaranteed in the initial control stage and also in case of characteristic deviations of the controlled system. Simulation results show that this control strategy is effective, practicable and superior to conventional PID control. [ABSTRACT FROM AUTHOR]

A two-stage structure model was developed for the vibration control of an actuator platform and a controller based on a three-layer neural network was applied to realize high performance control for the kickstand disturbance of a block forming machine. This paper presents a survey of the basic theory of the back-propagation(BP) neural network architecture including its architectural design, BP algorithm, the root mean square error (RMSE) and optimal model establishment. The situ-test data of the control system were measured by acceleration transducer and the experimental results indicates that the proposed method was effective. [ABSTRACT FROM AUTHOR]

This paper presents a neural network based control design to handle the stabilization of a class of multiple input nonlinear systems with time varying uncertain parameters while assuming that the range of each individual uncertain parameter is known. The proposed design approach allows incorporation of complex control performance measures and physical control constraints whereas the traditional adaptive control techniques are generally not applicable. The desired system dynamics are analyzed, and a collection of system dynamics data, that represents the desired system behavior and approximately covers the region of stability interest, is generated and used in the construction of the neural controller based on the proposed neural control design. Furthermore, the theoretical aspects of the proposed neural controller are also studied, which provides insightful justification of the proposed neural control design. The simulation study is conducted on a single-machine infinity-bus (SMIB) system with time varying uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of observer-based stabilization for time-delay systems. Both the state delay and input delay under consideration are assumed to be a constant time-delays, but not known exactly. A new design method is proposed for an observer-based controller with adaptation to the time-delays. The designed controller simultaneously contains both the current state and the past information of systems. The design for adaptation law to delay constants is more concise than the existing conclusions. The controller can be derived by solving a set of linear matrix inequalities (LMIs). [ABSTRACT FROM AUTHOR]

Environment model and communication time delays of a teleoperation system are variant usually, which will induce bad performance, even instability of the system. In this paper, neural network based multiple model adaptive predictive control method is proposed to solve this problem. The whole control system is composed of predictive controller and decision controller. First of all, neural network model set of any possible environment is built up, and time forward state observer based predictive controllers are designed for all models. In succession, decision controller is designed to adaptive switch among all predictive controllers according to performance target. This method can ensure stability and performance of the system. Finally, simulation results show effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Neural network controller using adaptation algorithm is a new and simple controller, in which a feedback network propagating the error is not required. So it can be applied to hardware easily. Nevertheless, our simulations show that while the order of controlled plant is high, some unstable phenomenon appear and we also find that sometimes the error is far from being satisfactory, although when the order of controlled plant is low. Moreover, the present adaptation algorithm can not solve this problem. In this paper we will give our derivation of adaptation algorithm used in the neural network controller and configuration of an adaptive neural network controller. Then give some simulation figures to illustrate defect for the new controller. Finally we will develop a hybrid neural network to solve the problem and improve the accuracy as well as reduce the cost to the least in the practical application. [ABSTRACT FROM AUTHOR]

In this paper, we first present a series of dynamic TS fuzzy subsystems to approximate a nonlinear singularly perturbed system. Then the reference model with same fuzzy sets is established. To make the states of the closed-loop system follow those of the reference model, a controller including of neuro-fuzzy adaptive and linear feedback term is designed. The linear feedback parameters can be solved by LMI approach. Adaptive term is used to compensate the uncertainty and alleviate the external disturbance. Lyapunov constitute techniques can prove the stability of the closed loop systems. The simulations results illustrate the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

This paper proposes to use a radial basis function (RBF) neural network in realising an adaptive control law for air/fuel ratio (AFR) regulation of automotive engines. The sliding mode control (SMC) structure is used and a new sliding surface is developed in the paper. The RBF network adaptation and the control law are derived using the Lyapunov method so that the entire system stability and the network convergence are guaranteed. The developed method is evaluated by computer simulation using the well-known mean value engine model (MVEM) and the effectiveness of the method is proved. [ABSTRACT FROM AUTHOR]

In this paper, a robust adaptive control scheme is proposed for a class of uncertain MIMO time delay systems based on backstepping method with Radical basis function(RBF) neural network. The system uncertainty is approximated by RBF neural networks, and a parameter update law is presented for approximating the system uncertainty. In each step, the control scheme is derived in terms of linear matrix inequalities (LMI's). A robust adaptive controller is designed using backstepping and LMI method based on the output of the RBF neural networks. Finally, an example is given to illustrate the availability of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, a robust neural networks adaptive control scheme is proposed for the stabilization of uncertain linear systems with time-varying delay and bounded perturbations. The uncertainty is assumed to be unknown continuous function without norm-bounded restriction. The perturbation is sector-bounded. Combined with liner matrix inequality method, neural networks and adaptive control, the control scheme ensures the stability of the close-loop system for any admissible uncertainty. [ABSTRACT FROM AUTHOR]

This paper presents a TSK-type recurrent fuzzy neural network (TRFNN) system and hybrid algorithm to control nonlinear uncertain systems. The TRFNN is modified from the RFNN to obtain generalization and fast convergence rate. The consequent part is replaced by linear combination of input variables and the internal variable- fire strength is feedforward to output to increase the network ability. Besides, a hybrid learning algorithm (GA_BPPSO) is proposed to increase the convergence, which combines the genetic algorithm (GA), back-propagation (BP), and particle swarm optimization (PSO). Several simulation results are proposed to show the effectiveness of TRFNN system and GA_BPPSO algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive fuzzy-neural network control problem is discussed for some uncertain MIMO nonlinear systemswith the block-triangular structure. The fuzzy-neural networks are utilized to approximate the virtual controllers, and by using backstepping technique, the direct adaptive FNN control scheme is developed. The proposed control method guarantees the closed-loop signals to be semiglobally uniformly ultimately bounded. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive control scheme with recurrent fuzzy neural network (RFNN) by using the temperature of the drying process for grain dryers. In this scheme, there are two RFNNs and two PI controllers. One RFNN with feedforeward and feedback connections of grain layer history position states predicts outlet moisture content (MPRFNN), and the other predicts the discharge rate of the dryer (RPRFNN). One PI controller adjusts the objective of the discharge rate by using MPRFNN, and the other adjusts the given frequency of the discharge motor to control the discharge rate of the grain dryer to reach its objective by using RPRFNN. The experiment is carried out by applying the proposed scheme on the control of a gain dryer with four stages to confirm its effectiveness. [ABSTRACT FROM AUTHOR]

Radial basis function (RBF) networks of Gaussian activation functions have been widely used in many applications due to its simplicity, robustness, good approximation and generalization ability, etc.. However, the training of such a RBF network is still a rather difficult task in the general case and the main crucial problem is how to select the number and locations of the hidden units appropriately. In this paper, we utilize a new kind of Bayesian Ying-Yang (BYY) automated model selection (AMS) learning algorithm to select the appropriate number and initial locations of the hidden units or Gaussians automatically for an input data set. It is demonstrated well by the experiments that this BYY-AMS training method is quite efficient and considerably outperforms the typical existing training methods on the training of RBF networks for both clustering analysis and nonlinear time series prediction. [ABSTRACT FROM AUTHOR]

Automatic communication signal (e.g., FM signal) classification and identification focus on finding the fine feature contained in the almost approximate noisy communication signal comprehensively identifying the the same or different version of transmitters in modern electronic warfare. Direct use of HOS becomes unavailable for on-line application because of its huge computation time and memory space especially in the case of high frequency FM signal. This paper presents a novel view to improve the HOS analysis efficiency by sub-sampling while preserving the noise-contaminated fine feature and eliminating the random Gaussian noise. FM signal-related feature bispectra are also introduced to translate the 2-D feature matching pattern to a 1-D one applicable for an optimal adaptive k-means iterative RBF classifier. Computer simulations show that this novel feature bispectra outperform AIB and SB in terms of computation time and recognition rate for on-line steady FM signal recognition. [ABSTRACT FROM AUTHOR]

This paper presents zero-crossing-based feature extraction for the speech recognition using neck-microphones. One of the solutions in noise-robust speech recognition is using neck-microphones which are not affected by the environmental noises. However, neck-microphones distort the original voice signals significantly since they only capture the vibrations of vocal tracts. In this context, we consider a new method of enhancing speech features of neck-microphone signals using zero-crossings. Furthermore, for the improvement of zero-crossing features, we consider to use the statistics of two adjacent zero-crossing intervals, that is, the statistics of two samples referred to as the second order statistics. Through the simulation for speech recognition using the neck-microphone voice command system, we have shown that the suggested method provides the better performance than other approaches using conventional speech features. [ABSTRACT FROM AUTHOR]

Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. One of the most commonly used techniques in data mining, Artificial Neural Networks provide non-linear predictive models that learn through training and resemble biological neural networks in structure. This paper deals with a new adaptive neural network model: a feed-forward higher order neural network with a new activation function called neuron-adaptive activation function. Experiments with function approximation and stock market movement analysis have been conducted to justify the new adaptive neural network model. Experimental results have revealed that the new adaptive neural network model presents several advantages over traditional neuron-fixed feed-forward networks such as much reduced network size, faster learning, and more promising financial analysis. [ABSTRACT FROM AUTHOR]

This paper presents an informative gene set selection approach to tumor diagnosis based on the Distance Sensitive Rival Penalized Competitive Learning (DSRPCL) algorithm and redundancy analysis. Since the DSRPCL algorithm can allocate an appropriate number of clusters for an input dataset automatically, we can utilize it to classify the genes (expressed by the gene expression levels of all the samples) into certain basic clusters. Then, we apply the post-filtering algorithm to each basic gene cluster to get the typical and independent informative genes. In this way we can obtain a compact set of informative genes. To test the effectiveness of the selected informative gene set, we utilize the support vector machine (SVM) to construct a tumor diagnosis system based on the express profiles of its genes. It is shown by the experiments that the proposed method can achieve a higher diagnosis accuracy with a smaller number of informative genes and less computational complexity in comparison with the previous ones. [ABSTRACT FROM AUTHOR]

This paper presents a new method of regularization in regression problems using a Besov norm (or semi-norm) acting as a regularization operator. This norm is more general smoothness measure to approximation spaces compared to other norms such as Sobolev and RKHS norms which are usually used in the conventional regularization methods. In our work, we also suggest a new candidate of the regularization parameter, that is, the trade-off between the data fit and the smoothness of the estimation function. Through the simulation for function approximation, we have shown that the suggested regularization method is effective and the estimated values of regularization parameters are close to the optimal values associated with the minimum expected risks. [ABSTRACT FROM AUTHOR]

This paper presents an efficient online sequential learning algorithm for extreme learning machine, which can learn data one by one. In this algorithm, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. In the online sequence, the algorithm updates the output-layer weights with a Givens QR decomposition based on the orthogonalized least square algorithm. Simulations on benchmark problems demonstrate that the algorithm produces much better generalization performance than another online sequential extreme learning machine algorithm, or sometimes it has good performance than primitive extreme learning machine algorithm. [ABSTRACT FROM AUTHOR]

In the human mind, high-order knowledge is categorically organized, yet the nature of its internal representation system is not well understood. While it has been traditionally considered that there is a single innate representation system in our mind, recent studies suggest that the representational system is a dynamic, capable of adjusting a representation scheme to meet situational characteristics. In the present paper, we introduce a new cognitive modeling framework accounting for the flexibility in representing high-order category knowledge. Our modeling framework flexibly learns to adjust its internal knowledge representation scheme using a meta-heuristic optimization method. It also accounts for the multi-objective and the multi-notion natures of human learning, both of which are indicated as very important but often overlooked characteristics of human cognition. [ABSTRACT FROM AUTHOR]

Machine learning, a cornerstone of intelligent systems, has typically been studied in the context of specific tasks, including clustering (unsupervised learning), classification (supervised learning), and control (reinforcement learning). This paper presents a learning architecture within which a universal adaptation mechanism unifies a rich set of traditionally distinct learning paradigms, including learning by matching, learning by association, learning by instruction, and learning by reinforcement. In accordance with the notion of embodied intelligence, such a learning theory provides a computational account of how an autonomous agent may acquire the knowledge of its environment in a real-time, incremental, and continuous manner. Through a case study on a minefield navigation domain, we illustrate the efficacy of the proposed model, the learning paradigms encompassed, and the various types of knowledge learned. [ABSTRACT FROM AUTHOR]

In finite mixture modelling, it is crucial to select the number of components for a data set. We have proposed an entropy regularized likelihood (ERL) learning principle for the finite mixtures to solve this model selection problem under regularization theory. In this paper, we further give an asymptotic analysis of the ERL learning, and find that the global minimization of the ERL function in a simulated annealing way (i.e., the regularization factor is gradually reduced to zero) leads to automatic model selection on the finite mixtures with a good parameter estimation. As compared with the EM algorithm, the ERL learning can go across the local minima of the negative likelihood and keep robust with respect to initialization. The simulation experiments then prove our theoretic analysis. [ABSTRACT FROM AUTHOR]

In this paper, the periodic solution for a class of Cohen-Grossberg neural networks with variable coefficients is discussed. By using inequality analysis technique and matrix theory, some new sufficient conditions are obtained to ensure the existence, uniqueness, global attractivity and exponential stability of the periodic solution. An example is given to show the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

In this paper, we study the global asymptotic stability properties of cellular neural networks with variable coefficients and time varying delays. We present sufficient conditions for the global asymptotic stability of the neural networks . The proposed conditions, which are applicable to all continuous nonmonotonic neuron activation functions and do not require the interconnection matrices to be symmetric, establish the relationships between network parameters of the neural systems and the delay parameters. Some examples show that our results are new and improve the previous results derived in the literature. [ABSTRACT FROM AUTHOR]

This paper presents a method of force/position control by using the backstepping and passivity strict-feedback neural networks technique; passivity monitor can evaluate stability of a system based on the concept of passivity. The parameters estimation for the design is made by the neural networks technology, using the decouple method and matrix transforming technology, decomposing the robot system as the position subsystem and the force subsystem, then the control law of these subsystems are designed respectively. The results obtained are satisfactory by using hybrid force and position control, the error is negligible and the global stability of the system can also be obtained. [ABSTRACT FROM AUTHOR]