A fuzzified neural network copes with fuzzy signals and/or weights so that the information about the uncertainty of input and output can be served in the training process. Since learning process is the main function o...
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A fuzzified neural network copes with fuzzy signals and/or weights so that the information about the uncertainty of input and output can be served in the training process. Since learning process is the main function of fuzzy neural networks, in this study, we focus on review and comparison of the existing learning algorithms, so that the theoretical achievement and the application agenda of each considered algorithm can be clarified from the aspects of computation complexity and accuracy. Two numerical examples of nonlinear mapping of fuzzy numbers and realization of fuzzy IF-THEN rules are used for illustration and analysis.
A simplified model that studies stimuli representation and the set of algorithms that let us analyze associative learning in some particular cases with predetermined values of the salience of the stimuli is presented....
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A simplified model that studies stimuli representation and the set of algorithms that let us analyze associative learning in some particular cases with predetermined values of the salience of the stimuli is presented. We simulate an experiment where rats were trained in a Morris pool to find a hidden platform in the presence of a single landmark. The results obtained agree with a previous study where it was found that the control acquired by a single landmark is different depending on its relative distance from the hidden platform. In this paper, some simplified equations of the associative learning model have been used. (C) 2008 Elsevier Ltd. All rights reserved.
A clustering algorithm for datasets with pairwise constraints using the Centroid Neural Network (***) is proposed in this paper. The proposed algorithm, referred to as the Centroid Neural Network with Pairwise Constra...
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A clustering algorithm for datasets with pairwise constraints using the Centroid Neural Network (***) is proposed in this paper. The proposed algorithm, referred to as the Centroid Neural Network with Pairwise Constraints (Cent. NN-PC) algorithm, utilizes *** as its backbone algorithm for data clustering and adopts a semi-supervised learning process for pairwise constraints. A newly formulated energy function is adopted from the original *** algorithm for the proposed ***-PC algorithm, introducing penalty terms for violating constraints. The weight update procedure of the proposed ***-PC algorithm finds optimal prototypes for the given dataset that minimize the quantization error while minimizing the number of violated constraints. In order to evaluate the performance of the proposed ***-PC algorithm, experiments on six different datasets from the UCI database and two bioinformatics datasets from the KEEL repository are carried out. The performance of the proposed algorithm is compared to that of the the Linear Constrained Vector Quantization Error (LCVQE) algorithm, one of the most commonly used algorithms for data clustering with pairwise constraints. In the experiments, five different numbers of pairwise constraints are utilized to evaluate the clustering performance with constraints of different sizes. The results show that the proposed ***-PC algorithm outperforms the LCVQE algorithm on most performance criteria, including the total quantization error, the number of violated constraints, and on the three performance metrics of the classification accuracy rate, F-score, and NMI measure outcome. The experiments also show that ***-PC provides much more stable clustering results at an improved operational speed compared to LCVQE.
Defensive and offensive capabilities are both significant in communication confrontation games. By exploiting the above two capabilities, a new confrontation mechanism in the spectrum domain between two opposing teams...
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Defensive and offensive capabilities are both significant in communication confrontation games. By exploiting the above two capabilities, a new confrontation mechanism in the spectrum domain between two opposing teams denoted as the blue team (BT) and red team (RT), is designed. The basic idea is that by sacrificing parts of ally performance to severely deteriorate the opponent side communications. Specifically, a friendly and smart jammer (assuming in the BT) is deployed to weaken opponent (i.e., members in the RT) communications without causing great damages to other BT members, while the smart RT members try to evade the jamming and alleviate mutual interference. The interactions among the friendly jammer and other nodes are modeled as a Stackelberg game, with each player seeking for their respective utility maximization. We prove that each sub-game is an exact potential game. To efficiently search for the equilibrium solutions, a parallel log-linear learning algorithm is proposed, based on which each user intelligently decides their spectrum access policies. Numerical results demonstrate that: 1) RT communications are effectively suppressed;meanwhile, mutual interference among ally BT communication pairs is significantly alleviated;2) the proposed algorithm achieves a close-to-optimal solution;3) compared with the current state of solutions, i.e., random selection, stochastic learning automata, our algorithm performs better in terms of both utility and convergence.
This paper describes a new type of learning control method for precision velocity control of servomotors suffering from significant disturbance torque. The disturbance torque under consideration is assumed to be perio...
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This paper describes a new type of learning control method for precision velocity control of servomotors suffering from significant disturbance torque. The disturbance torque under consideration is assumed to be periodic in time and nonlinear in system states, but possibly non-Lipschitzian. Based on the property that the learning system tends to oscillate in the steady state, the proposed learning algorithm iteratively generates a feedforward input to cancel the effect of the disturbance torque. Thereby, it can eventually drive the steady-state velocity error to zero. In order to demonstrate the generality of the proposed method, we present a rigorous analysis for the convergence of the proposed learning algorithm. The effectiveness of the proposed method is demonstrated by simulation and experiment.
With the prevalence of GPS-enabled smart phones, Location Based Social Network (LBSN) has emerged and become a hot research topic during the past few years. As one of the most important components in LBSN, Points-of-I...
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With the prevalence of GPS-enabled smart phones, Location Based Social Network (LBSN) has emerged and become a hot research topic during the past few years. As one of the most important components in LBSN, Points-of-Interests (POIs) has been extensively studied by both academia and industry, yielding POI recommendations to enhance user experience in exploring the city. In conventional methods, rating vectors for both users and POIs are utilized for similarity calculation, which might yield inaccuracy due to the differences of user biases. In our opinion, the rating values themselves do not give exact preferences of users, however the numeric order of ratings given by a user within a certain period provides a hint of preference order of POIs by such user. Firstly, we propose an approach to model users preference by employing utility theory. Secondly, We devise a collection-wise learning method over partial orders through an effective stochastic gradient descent algorithm. We test our model on two real world datasets, i.e., Yelp and TripAdvisor, by comparing with some state-of-the-art approaches including PMF and several user preference modeling methods. In terms of MAP and Recall, we averagely achieve 15% improvement with regard to the baseline methods. The results show the significance of comparative choice in a certain time window and show its superiority to the existing methods. (C) 2015 Elsevier Ltd. All rights reserved.
This work establishes a new upper bound on the number of samples sufficient for PAC learning in the realizable case. The bound matches known lower bounds up to numerical constant factors. This solves a long-standing o...
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This work establishes a new upper bound on the number of samples sufficient for PAC learning in the realizable case. The bound matches known lower bounds up to numerical constant factors. This solves a long-standing open problem on the sample complexity of PAC learning. The technique and analysis build on a recent breakthrough by Hans Simon.
In this paper, we propose a novel learning method of two-channel linear filter for a target sound extraction in a non-stationary noisy environment using a two-channel microphone array. The method is based oil a correl...
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In this paper, we propose a novel learning method of two-channel linear filter for a target sound extraction in a non-stationary noisy environment using a two-channel microphone array. The method is based oil a correlation coefficient between received sounds of two microphones. The cue signal, which has a correlation with a variation of S/N of the received sounds, is generated using the correlation coefficient and is applied to the learning. By several computer simulation results, a superior performance of the proposed method even at the consonant section of the speech signal is presented in comparison with the previously proposed method. (c) 2006 Wiley Periodicals, Inc.
Extracting of fuzzy rules and learning of fuzzy membership functions are important and difficult problems in the designing of fuzzy *** of membership functions for Takagi-Sugeno fuzzy model is *** propose a new learni...
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Extracting of fuzzy rules and learning of fuzzy membership functions are important and difficult problems in the designing of fuzzy *** of membership functions for Takagi-Sugeno fuzzy model is *** propose a new learning approach for fuzzy membership functions based on simulated *** learning algorithm is able to determine the parameters of a fuzzy membership ***,an example is given to demonstrate the validity of this algorithm.
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