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In machine vision, template matching is key component and used usefully in various tasks such as pick and place, mark identification, and alignment. In this paper, we propose fast template matching algorithm using edge projection. Proposed algorithm reduces the search problem from 2D into 1D using edge projection within the 2D template area. By this, it could effectively reduce the computational burden. Also, it gives comparable discriminating power compared to template matching using intensity. In this paper, rotation and translation search is implemented to cope with typical machine vision application where the height between camera and target object is fixed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for the recognition of passports based on a FCM-based RBF network. First, for the extraction of individual codes for recognizing, this paper targets code sequence blocks including individual codes by applying Sobel masking, horizontal smearing and a contour tracking algorithm on the passport image. As the last step, individual codes are recovered and extracted from the binarized areas by applying CDM masking and vertical smearing. This paper also proposes a FCM-based RBF network that adapts the FCM algorithm for the middle layer. This network is applied to the recognition of individual codes. The results of the experiments for performance evaluation on the real passport images showed that the proposed method has the better performance compared with other approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a partitional approach for estimating null value (1) Firstly, we utilize stepwise regression to select the important attributes from the database. (2) Secondly, we use a partitional approach to build the data category. The data partitioned by the first two important attributes. (3) Thirdly, we apply the clustering method to cluster output data. (4) Fourthly, Calculate the degree of influential between the attributes. There are two ways to calculate the degree of influential. One is correlation coefficient and the other is regression coefficients. (5) To verify our method, this paper utilizes a practical human resource database in Taiwan, and Mean of Absolute Error Rate (MAER) as evaluation criterion to compare with other methods; it is shown that our proposed method proves better than other methods for estimating null values in relational database systems. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the conditioning issue in D-S evidence theory in multi-dimensional space. Based on Dempster conditioning, Bayes' rule and product rule, which are similar to that in probability theory, are presented in this paper. Two kinds of conditional independence called weak conditional independence and strong conditional independence are introduced, which can significantly simplify the inference process when evidence theory is applied to practical application. [ABSTRACT FROM AUTHOR]

In this paper we improve on the supervised classification method developed in Kornienko et al. (2002) by the introduction of Principal Components Analysis to the inference process. We also extend the classifier from dealing with binomial (two-class) problems only to multinomial (multi-class) problems. The application to which the MML criterion has been applied in this paper is the classification of objects via a linear hyperplane, where the objects are able to come from any multi-class distribution. The inclusion of Principal Component Analysis to the original inference scheme reduces the bias present in the classifier's search technique. Such improvements lead to a method which, when compared against three commercial Support Vector Machine (SVM) classifiers on Binary data, was found to be as good as the most successful SVM tested. Furthermore, the new scheme is able to classify objects of a multiclass distribution with just one hyperplane, whereas SVMs require several hyperplanes. Keywords: Machine Learning, Knowledge discovery and data mining. [ABSTRACT FROM AUTHOR]

This paper provides a framework of using news articles and economic data to model the exchange rate changes between Euro and US dollars. Many studies have conducted on the approach of regressing exchange rate movement using numerical data such as macroeconomic indicators. However, this approach is effective in studying the long term trend of the movement but not so accurate in short to middle term behaviour. Recent research suggests that the market daily movement is the result of the market reaction to the daily news. In this paper, it is proposed to use text mining methods to incorporate the daily economic news as well as economic and political events into the prediction model. While this type of news is not included in most of existing models due to its non-quantitative nature, it has important influence in short to middle terms of market behaviour. It is expected that this approach will lead to an exchange rate model with improved accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces a classification system that exploits the content information as well as citation structure for scientific paper classification. The system first applies a content-based statistical classification method which is similar to general text classification. We investigate several classification methods including K-nearest neighbours, nearest centroid, naive Bayes and decision trees. Among those methods, the K-nearest neighbours is found to outperform others while the rest perform comparably. Using phrases in addition to words and a good feature selection strategy such as information gain can improve system accuracy and reduce training time in comparison with using words only. To combine citation links for classification, the system proposes an iterative method to update the labellings of classified instances using citation links. Our results show that, combining contents and citations significantly improves the system performance. [ABSTRACT FROM AUTHOR]

In content-based image retrieval (CBIR), the user usually poses several labelled images and then the system attempts to retrieve all the images relevant to the target concept defined by these labelled images. It may be helpful if the system can return relevant images where the regions of interest (ROI) are explicitly located. In this paper, this task is accomplished with the help of multi-instance learning techniques. In detail, this paper proposes the CkNN-ROI algorithm, which regards each image as a bag comprising many instances and picks from positive bag the instance that has great chance to meet the target concept to help locate ROI. Experiments show that the proposed algorithm can efficiently locate ROI in CBIR process. [ABSTRACT FROM AUTHOR]

Minimal change is a fundamental principle for modelling system dynamics. In this paper, we study the issue of minimal change for Computational Tree Logic (CTL) model update. We first consider five primitive updates which capture the basic update operations in the CTL model. Based on these primitive updates, we then define the minimal change criteria for CTL model update and develop formal algorithms that embed the underlying minimal change principle. We also present the well known microwave oven scenario to demonstrate our update algorithms. Our work presented in this paper can be viewed as the first formalization towards an integration of model checking and model updating for system modification. [ABSTRACT FROM AUTHOR]

This paper presents a method that enable a domestic robot to show emotions with its facial expressions. The previous methods using built-in facial expressions were able to show only scanty face. To express faces showing complex emotion, mixed emotions and different strengths of emotions, more facial expressions are needed. We have therefore developed a system that converts emotions into "Ifbot" robot's facial expressions automatically. They are created from emotion parameters, which represent its emotions. Content Areas: Entertainment and AI, robotics. [ABSTRACT FROM AUTHOR]

In this paper three machine learning approaches, Neural Networks (NN), Support Vector Machines (SVM) and Neural Fuzzy Networks (FuNN) are used to extract rules and assess the reliability of complex networks. For NN and SVM models the TREPAN approach is proposed as a valid tool for extracting rules whereas the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for tuning a previous set of rules derived by a fuzzy inference system and neural network approach. [ABSTRACT FROM AUTHOR]

In this paper we present an effective recommendation algorithm using a refined neighbor selection and attributes information on the goods. The proposed algorithm exploits the transitivity of similarities using a graph approach. The algorithm also utilizes the attributes of the items. The experiment results show that the recommendation system with the proposed algorithm outperforms other systems and it can also overcome the very large-scale dataset problem without deteriorating prediction quality. [ABSTRACT FROM AUTHOR]

Synchronization in chaotic oscillatory systems has a wide array of applications in biology, physics and communication systems. Over the past 10 years there has been considerable interest in the synchronization properties of small-world and scale-free networks. In this paper, we define the fitness of a configuration of coupled oscillators as its ability to synchronize. We then employ an optimization algorithm to determine network structures that lead to an enhanced ability to synchronize. The optimized networks generally have low clustering, small diameters, short path-length, are disassortative, and have a high degree of homogeneity in their degree and load distributions. [ABSTRACT FROM AUTHOR]

This paper presents the Intelligent Power Quality Disturbance Analysis (IPQDA) software tool that is designed for an automatic analysis of power quality (PQ) disturbance. The main capabilities of the software include analysis of disturbance waveforms, identification of a particular type of disturbance and notification of a disturbance. Another important feature of the program is that it can automatically send email or short messaging notifications upon identification of a disturbance to alert the system operator of a disturbance. [ABSTRACT FROM AUTHOR]

Link discovery is an emerging research direction for extracting evidences and links from multiple data sources. This paper proposes a self-organizing framework for discovering links from multi-relational databases. It includes main functional modules for developing adaptive data transformers and representation specification, multi-relational feature construction, and self-organizing multi-relational correlation and link discovery algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel particle swarm optimization (PSO) for solving constrained optimization problems. Based upon the acceptable assumption that any feasible solution is better than any infeasible solution, a new mechanism for constraints handling is incorporated in the standard particle swarm optimization. In addition to the mechanism of constraints handling, a mutation strategy to increase population diversity is added to the proposed algorithm to improve convergence. Experimental results compared with genetic algorithm and a standard PSO show that the proposed algorithm is a desirable and competitive algorithm for solving constrained optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents microcontroller based autotuning proportional-integral-derivative (PID) controller for an oven designed as an experiment set. Different types of autotuning PID controller methods have been examined. Proportional, P, control method has been applied first. Relay and integral square time error criterion (ISTE) tuning methods are used as autotuning PID method. For relay tuning method, proportional (P), proportional-integral (PI) and proportional-integral-derivative (PID) and for ISTE disturbance (PI, PID) have been used. These methods have been applied to the experiment set which is an FODPT (First Order Plus Dead Time) system. To be able to control this system a digital signal processing card is designed. PIC17C44 is used as microcontroller and ADS1212 is used as A/D converter. And the results are discussed to define which controller is the best for this experiment set. Key words: Adaptive control, autotuning PID methods, temperature control. [ABSTRACT FROM AUTHOR]

Auto-playing programs are often used on behalf of human players in a MMORPG(Massively Multi-player Online Role Playing Game). By playing automatically and continuously, it helps to speed up the game character's level-up process. However, the auto-playing programs, either software or hardware, do harm to games servers in various ways including abuse of resources. In this paper, we propose a way of detecting the auto programs by analyzing the window event sequences produced by the game players. In our proposed method, the event sequences are transformed into a set of attributes, and various learning algorithms are applied to classify the data represented by the set of attribute values into human or auto player. The results from experiments with several MMORPGs show that the Decision Tree learning with proposed method can identify the auto-playing programs with high accuracy. Keywords: Data Mining, Entertainment and AI, Machine Learning, Intelligent Data Analysis. [ABSTRACT FROM AUTHOR]

In this paper, we present a segment-based stereo matching algorithm using adaptive variable windows and dynamic programming with a robust disparity. We solve the problem of window shape and size using adaptive line masks and adaptive rectangular windows which are constrained by segments and visibility that reduces ambiguity produced by the occlusion in the computation window. In dynamic programming, we also propose the method that selects an efficient occlusion penalty. [ABSTRACT FROM AUTHOR]

In this paper we propose a clustering algorithm called s-Cluster for analysis of gene expression data based on pattern-similarity. The algorithm captures the tight clusters exhibiting strong similar expression patterns in Microarray data,and allows a high level of overlap among discovered clusters without completely grouping all genes like other algorithms. This reflects the biological fact that not all functions are turned on in an experiment, and that many genes are co-expressed in multiple groups in response to different stimuli. The experiments have demonstrated that the proposed algorithm successfully groups the genes with strong similar expression patterns and that the found clusters are interpretable. Keywords: data mining, bioinformatics, Microarray data analysis, clustering. [ABSTRACT FROM AUTHOR]

This paper describes the personal locating system in image sequence using a vision-based augmented reality technique which allows the user to navigate an unfamiliar and unknown place in an office environment. For identifying personal location in image sequences, the system uses a color histogram matching method and location model. The results are overplayed on the user's view through AR technique. This system is applicable to guide an application. [ABSTRACT FROM AUTHOR]

Paper deals with the concept of information fusion and its application to the contextual pattern recognition task. The concept of the recognition based on the probabilistic model are presented. The machine learning algorithm based on statistical tests for the recognition of controlled Markov chains is shown. Some experimental results of obtained methods are shown. [ABSTRACT FROM AUTHOR]

This paper proposes a two-phase diversity control approach to prevent the common problem of the loss of diversity in Genetic Programming with Automatically Defined Functions. While most recent work focuses on diagnosing and remedying the loss of diversity, this approach aims to prevent the loss of diversity in the early stage through a refined diversity control method and a fully covered tournament selection method. The results on regression tasks suggest that these methods can effectively improve the system performance by reducing the incidences of premature convergence and the number of generations needed for finding an optimal solution. [ABSTRACT FROM AUTHOR]

This paper describes a vision system for extracting and recognising partially occluded 2D visual landmarks. The system is developed based on the traditional template matching approach and a memory feedback modulation (MFM) mechanism. It identifies the obscured portions and selectively enhances non-occluded areas of the landmark, while simultaneously suppressing background clutters of the bottom-up edge processed input images. The architecture has been tested with a large number of real images with varying levels of landmark concealment and further evaluated using a vision-based navigating robot in the laboratory environment. [ABSTRACT FROM AUTHOR]

This paper presents an approach of target word selection for Korean verbs based on lexical knowledge contained in a Korean-English bilingual dictionary and WordNet. We focus on deciding which translation of the target word is the most appropriate using the measure of semantic relatedness between possible translations of target word and some indicative clue words. With five Korean ambiguous verbs, we report an average accuracy of 51% that outperforms the default baseline performance and previous works. [ABSTRACT FROM AUTHOR]

This paper proposes a new tracking algorithm using the Kalman estimator based color SSD algorithm. The Kalman estimator includes the color information as well as the position and size of the face region in its state vector, to take care of the variation of skin color while faces are moving. Based on the estimated face position, the color SSD algorithm finds the face matching with the one in the previous frame even when the color and size of the face region vary. The features of a face region extracted by the color SSD algorithm are used to update the state of the Kalman estimator. In the experiments, it has been shown that the proposed algorithm traces multiple faces successfully even when they are overlapped for a moment. [ABSTRACT FROM AUTHOR]

Principal Component Analysis (PCA) plus Linear Discriminant Analysis (LDA) (PCA+LDA) and LDA/QR are both two-stage methods that deal with the Small Sample Size (SSS) problem in traditional LDA. When applied to face recognition under varying lighting conditions and different facial expressions, neither method may work robustly. Recently, resampling, a technique that generates multiple subsets of samples from the training set, has been successfully employed to improve the classification performance of the PCA+LDA classifier. In this paper, stimulated by such success, we propose a resampling LDA/QR method to improve LDA/QR's performance. Furthermore, taking advantage of the difference between LDA/QR and PCA+LDA, we incorporate them by resampling for face recognition. Experimental results on AR dataset verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper proposes a robust face recognition system, by providing a strong discrimination power and cancelable mechanism to biometrics data. Fisher's Linear Discriminant uses pseudo Zernike moments to derive an enhanced feature subset. On the other hand, the revocation capability is formed by the combination of a tokenized pseudo-random data and the enhanced template. The inner product of these factors generates a user-specific binary code, face-Hash. This two-factor basis offers an extra protection layer against biometrics fabrication since face-Hash authenticator is replaceable via token replacement. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel method about how to recognize the human action/activity in video using motion verbs based on WordNet. We provide a more deterministic mapping, and then we have extended WordNet with a small, fixed vocabulary of highly salient attribute for human motion description. [ABSTRACT FROM AUTHOR]

The majority of Internet applications requires interaction between various entities through the network, in order to exchange data and to distribute tasks. The client/server model where exchanges are given by distant interactions is the most used model. It has the disadvantage of increasing network traffic by exchanging intermediary information. In this paper we show that in some cases it is better to send the code to the server and to work locally. We study two models of communication (the client/server and the mobile agent) and propose a hybrid one, where agent uses the two models and construct the optimal policy according to the characteristic of the network. The Markov decision processes are used to calculate the optimal policy for agent displacement. This policy is applied by the agent in order to decrease network traffic. [ABSTRACT FROM AUTHOR]

Plot planning is one of the key steps in automatic generation of computer animation, aiming at converting the abstract plots of a story into a series of concrete actions. This paper presents the architecture of plot planning model, in which we employ an agent-based method to develop the abstract plot. In our agent hierarchy, there are two kinds of agents: drama agent and plot agent, both of which can develop the abstract plot according to their knowledge and strategies. Furthermore, to avoid the irrationality of the results, we address a validity checking mechanism to ensure the consistency of the concrete actions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new agent architecture (BlueAgents) with the Dynamic Pricing and Maximum Profit Strategy (DPMPS). BlueAgents we design shows the flow of the trading as request for quotes, offer and order based on the time frame clearly and we describe the function modules that BlueAgents is composed of. One of the main foci of the decision making in a supply chain management for the trading agent competition (TAC SCM) is an optimization of the offer price under the uncertainty. Price is an important factor for maximizing benefit through the bidding process in competition with other agents. DPMPS is a benchmarking model to predict dynamically realistic optimal offer prices for bidding between an estimated offer price and regressed price of current market after estimating an optimized offer price for maximizing profit. [ABSTRACT FROM AUTHOR]

In this paper, we employ Answer Set Programming to deal with many complex issues associated with the distributed authorization along the trust management approach. Using our approach, we can not only express nonmonotonic delegation policies which have not been considered in previous approaches, but also represent the delegation with depth, separation of duty, and positive and negative authorizations. We also investigate basic computational properties related to our approach and discuss a case study to illustrate the application of our approach in a distributed environment. [ABSTRACT FROM AUTHOR]

In this paper, we present a new model for the skeleton driven limb animation, which is composed of three layers: linear bones, volumetric bones and coarse volumetric control lattice. Volumetric bones are driven by linear bones using geometric method and the coarse volumetric control lattice is driven by volumetric bones using finite element method. In order to compute faster, we perform linearized simulations. The limb is embedded in the volumetric control lattice, and the surface of limb is computed using linear interpolation method. PCG solver is used to solve the large linear system of equations. We can obtain realtime simulations and realize the motions such as bend and torsion of limb. [ABSTRACT FROM AUTHOR]

Word sense disambiguation has sense division and sense selection as its two sub-problems. An appropriate solution to the sense division problem is usually dependent on the application being pursued. In the context of machine translation, picking the correct translation for a word among multiple candidates, is known as target word selection. The work in this paper uses the Web as the main knowledge source to address the difficulty of making a target word selection based on statistics, which are normally drawn from rather limited corpora. The proposed approach uses simple and easily accessible web statistics-search engine hits (number of document returned for a particular query) to demonstrate the great potential of the Web as a knowledge source for word sense disambiguation. Our experimental results so far are very encouraging. [ABSTRACT FROM AUTHOR]

In this paper we propose an approach to integrate protein information from various data sources by defining a Protein Ontology. Protein Ontology provides the technical and scientific infrastructure and knowledge to allow description and analysis of relationships between various proteins. Protein Ontology uses relevant protein data sources of information like PDB, SCOP, and OMIM. Protein Ontology describes: Protein Sequence and Structure Information, Protein Folding Process, Cellular Functions of Proteins, Molecular Bindings internal and external to Proteins, and Constraints affecting the Final Protein Conformation. Details about Protein Ontology are available online at http://www.proteinontology.info/. Keywords: Protein Ontology, Biomedical Ontologies, Knowledge Representation, Information Retrieval, Data Integration. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent decision support framework for an effective e-service management. The proposed framework integrates case and rule based reasonings and multi criteria decision-making techniques in fuzzy environment for a real-time decision-making, which is dealing with uncertain and imprecise decision situations. The framework potentially leads to more accurate, flexible and efficient retrieval of alternatives that are most similar and most useful to the current decision situation. [ABSTRACT FROM AUTHOR]

This paper proposes an idea for constructing a personalized recommendation system for the Electronic Program Guide (EPG). This system would use a basic statistics method with feedback process to predict television programs. We have applied this method to personal prediction of online Internet Electronic Program Guide (IEPG). The system was found to have good accuracy and dynamically adaptive capability. [ABSTRACT FROM AUTHOR]

In this paper a new linear matrix inequality (LMI) based design method for T-S fuzzy classifier is proposed. The various design factors including structure of fuzzy rule and various parameters should be considered to design T-S fuzzy classifier. To determine these design factors, we describe a new and efficient two-step approach that leads to good results for classification problem. At first, LMI based fuzzy clustering is applied to obtain compact fuzzy sets in antecedent. Then consequent parameters are optimized by a LMI optimization method. [ABSTRACT FROM AUTHOR]

This paper proposes a new fuzzy rule-based classifier equipped with a Bayes rule consequent. The main features of our approach are no requirement on the covariance matrices structure and their avoidance of singularity; the expansion in unimodal densities to multimodal ones; and the fuzzy set analysis for measuring the qualities of features. Two tools are exploited in constructing the proposed classifier: the iterative pruning algorithm for removing the irrelevant features and the gradient descent method for training the related parameters. [ABSTRACT FROM AUTHOR]

In this paper a multi-item fuzzy inventory model under total production cost, total storage space and number of orders constraints is solved with a Genetic Algorithm. In this model, the production cost and set up cost are directly proportional to the respective quantities, unit production cost is inversely related to the demand and set up cost is assumed to vary directly with lot size. Also Shortages are allowed. However this approach has been applied to solve the model under fuzzy objective of cost minimization and imprecise constraints on storage space, number of orders and production cost with imprecise inventory costs. This model has been formulated as FNLP problem and then converted to equivalent crisp decision making problems and solved by a Genetic Algorithm. Finally the model is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

This paper gives a comparative study of feature selection methods in spam-mail filtering. In our experiment, the fuzzy inference method showed about 6% and 10% improvements over information gain and χ2-test as a feature selection method in terms of the average error rate which is more important than typical information retrieval measures. Since it is not easy to reduce error rate, our work can be regarded as a meaningful research for email users suffering from unsolicited emails flooding indiscriminately. [ABSTRACT FROM AUTHOR]

Directed mutation has been proposed for improving the convergence speed of GAs on problems involving real-valued alleles. This paper proposes a directed mutation approach based on the momentum term used in gradient descent training of neural networks. Mutation-with-momentum is compared against gaussian mutation and is shown to regularly result in improvements in performance during early generations. A hybrid of momentum and gaussian mutation is shown to outperform either individual approach to mutation. [ABSTRACT FROM AUTHOR]

This paper reports on a study which compared the convergence of different-leveled structured Genetic Algorithms (sGAs) used to generate Neural Networks (NNs). Results suggest that sGAs are more effective at generating NNs compared to simple GAs. Using more than 2 sGA levels does not always yield a better error curve, as each added level provides a diminishing performance increase. The optimum number of sGA levels for NN generation is problem specific, though higher level sGAs tend to produce more efficient NNs. SGAs with more levels seem to perform better for difficult NN problems with complex features and large boundary conditions which create more redundancy. When the emphasis on complexity is increased in the fitness function, the error curve variations between different level configurations become more pronounced. [ABSTRACT FROM AUTHOR]

This paper presents a design method of the automatic loop-shaping which couples up manual loop-shaping method to genetic algorithms (GAs) in quantitative feedback theory (QFT). The loop-shaping is currently performed in computer aided design environments manually, and moreover, it is usually a trial and error procedure. To solve this problem, an automatic loop-shaping method based on GAs and evolutionary computation is developed and a benchmark example is used to examine the performance of the proposed automatic loop-shaping compared with that of the manual loop-shaping and similar other research. [ABSTRACT FROM AUTHOR]

Loop is an important structure in human written programs. However, it is seldom used in the evolved programs in genetic programming (GP). This paper describes an approach to the use of while-loop structure in GP for the factorial and the artificial ant problems. Two different forms of the while-loop structure, count-controlled loop and event-controlled loop, are investigated. The results suggest that both forms of the while-loop structure can be successfully evolved in GP, the system with the while-loop structure is more effective and more efficient than the standard GP system for the two problems, and the evolved genetic programs with the loop-structure are much easier to interpret. [ABSTRACT FROM AUTHOR]

In this paper, we introduce new architectures and comprehensive design methodologies of Evolutionally optimized Fuzzy Neural Networks (EoFNN). The proposed dynamic search-based GAs leads to rapidly optimal convergence over a limited region or a boundary condition. The proposed EoFNN is based on the Fuzzy Neural Networks (FNN) with the extended structure of fuzzy rules being formed within the networks. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and modified quadratic takes into consideration. The structure and parameters of the EoFNN are optimized by the dynamic search-based GAs. [ABSTRACT FROM AUTHOR]

This paper presents the application of Differential Evolution (DE), an Evolutionary Computation method, for the optimization of adaptive FIR filter weights. This method is robust and easy to use and requires a few control variables. Since the algorithm uses differential property, it has a good convergence speed and also quite robust in the case of noise due to parallel structure. In the simulation study three well-known error functions are used to test the performance of proposed method in the Adaptive Linear Combiner (ALC) design. [ABSTRACT FROM AUTHOR]

In this paper, an artificial neural networks identifier and a fuzzy logic controller for ITU Triga Mark-II Nuclear Research Reactor is presented. Three parted control function is used as a reference trajectory that the fuzzy logic controller tracks. The nonlinear behavior of the reactor is identified by using generalized neural networks. The validity of the proposed identification model is tested by comparing these results with the ones obtained by YAVCAN code. The effectiveness of the controller is demonstrated on the neural network model. [ABSTRACT FROM AUTHOR]

This paper presents a multicriteria DRC scheduler in order to select appropriate dispatching rules. This scheduler integrates several tools, namely; a simulation model, a backpropagation neural network (BPNN) and a Multicriteria decision aid (MCDA) method. Simulation is used to collect predefined performance measures corresponding to decision rule set and system state variables. Because of the time consuming nature of simulation, BPNN is used to obtain the performance measures for each alternative schedule. In order to compare the system performance between all alternatives, the evaluation of each alternative is performed by PROMETHEE, which is a well-known MCDA method. By means of a realistic numerical example, the proposed methodology is proved to be an effective method in a DRC manufacturing system. [ABSTRACT FROM AUTHOR]