Catálogo de publicaciones - libros

A new decision information fusion algorithm based on the fuzzy neural networks, which introduces fuzzy comprehensive assessment into traditional decision information fusion technology under the “” decision architecture, is proposed. The process of fusion is composed of the comprehensive operation and the global decision through fusing the local decision of multiple sensors for obtaining the global decision of the concerned object at the fusion center. In the practical application, the algorithm has been successfully applied in the temperature fault detection and diagnosis system of hydroelectric simulation system of Jilin Fengman. In the analysis of factual data, the performance of the algorithm precedes that of the traditional diagnosis method.

This paper presents the equalization of channel distortion by using a Nonlinear Neuro-Fuzzy Network (NNFN). The NFNN is constructed on the basis of fuzzy rules that incorporate nonlinear functions. The learning algorithm of NNFN is presented. The NFNN is applied for equalization of channel distortion of time-invariant and time-varying channels. The developed equalizer recovers the transmitted signal efficiently. The performance of NNFN based equalizer is compared with the performance of other nonlinear equalizers. The effectiveness of the proposed system is evaluated using simulation results of NNFN based equalization system.

Many adaptive schemes for controlling the probabilities of crossover and mutation in genetic algorithms with fuzzy logic have been reported in recent years. However, there has not been known work on comparative studies of these algorithms. In this paper, several fuzzy genetic algorithms are briefly summarized first, and they are studied in comparison with each other under the same simulation conditions. The simulation results are analyzed in terms of search speed and search quality.

In this paper, a two-level decentralized decision-making problem is formulated as fuzzy random dependent-chance bilevel programming. We define the fuzzy random Nash equilibrium in the lower level problem and the fuzzy random Stackelberg-Nash equilibrium of the overall problem. In order to find the equilibria, we propose a hybrid intelligent algorithm, in which neural network, as uncertain function approximator, plays a crucial role in saving computing time, and genetic algorithm is used for optimization. Finally, we apply the fuzzy random dependent-chance bilevel programming to hierarchical resource allocation problem for illustrating the modelling idea and the effectiveness of the hybrid intelligent algorithm.

Based on value-at-risk (VaR) criteria, this paper presents a new class of two-stage fuzzy programming models. Because the fuzzy optimization problems often include fuzzy variables defined through continuous possibility distribution functions, they are inherently infinite- dimensional optimization problems that can rarely be solved directly. Thus, algorithms to solve such optimization problems must rely on intelligent computing as well as approximating schemes, which result in approximating finite-dimensional optimization problems. Motivated by this fact, we suggest an approximation method to evaluate critical VaR objective functions, and discuss the convergence of the approximation approach. Furthermore, we design a hybrid algorithm (HA) based on the approximation method, neural network (NN) and genetic algorithm (GA) to solve the proposed optimization problem, and provide a numerical example to test the effectiveness of the HA.

With the development of network technology and E-commerce, more and more enterprises have accepted the management pattern of E-commerce. In order to meet the personalized needs of customers in E-supermarket, customer classification based on their interests is a key technology for developing personalized E-commerce. Therefore, it is highly needed to have a personalized system for extracting customer features effectively, and analyzing customer interests. In this paper, we proposed a new method based on the modified fuzzy neural network to group the customers dynamically according to their Web access patterns. The results suggest that this clustering algorithm is effective and efficacious. Taking one with another, this new proposed approach is a practical solution to make more visitors become to customers, improve the loyalty degree of customer, and strengthen cross sale ability of websites in E-commerce.

Consumer demand for intelligent battery charges is increasing as portable electronic applications continue to grow. Fast charging of battery packs is a problem which is difficult, and often expensive, to solve using conventional techniques. Conventional techniques only perform a linear approximation of a nonlinear behavior of a battery packs. The battery charging is a nonlinear electrochemical dynamic process and there is no exact mathematical model of battery. Better techniques are needed when a higher degree of accuracy and minimum charging time are desired. In this paper we propose soft computing approach based on fuzzy recurrent neural networks (RFNN) training by genetic algorithms to control batteries charging process. This technique does not require mathematical model of battery packs, which are often difficult, if not impossible, to obtain. Nonlinear and uncertain dynamics of the battery pack is modeled by recurrent fuzzy neural network. On base of this FRNN model, the fuzzy control rules of the control system for battery charging is generated. Computational experiments show that the suggested approach gives least charging time and least T-T results according to the other intelligent battery charger works.

This paper proposes the type-2 fuzzy neural network system (type-2 FNN) which combines the advantages of type-2 fuzzy logic systems (FLSs) and neural networks (NNs). For considering the system uncertainties, we use the type-2 FLSs to develop a type-2 FNN system. The previous results of type-1 FNN systems can be extended to a type-2 one. Furthermore, the corresponding learning algorithm is derived by input-to-state-stability (ISS) approach. Nonlinear system identification is presented to illustrate the effectiveness of our approach.

Fuzzy Petri net (FPN) is a powerful modeling tool for fuzzy production rules based knowledge systems. But it is lack of learning mechanism, which is the main weakness while modeling uncertain knowledge systems. Fuzzy neural Petri net (FNPN) is proposed in this paper, in which fuzzy neuron components are introduced into FPN as a sub-net model of FNPN. For neuron components in FNPN, back propagation (BP) learning algorithm of neural network is introduced. And the parameters of fuzzy production rules in FNPN neurons can be learnt and trained by this means. At the same time, different neurons on different layers can be learnt and trained independently. The FNPN proposed in this paper is meaningful for Petri net models and fuzzy systems.

This paper aims for the development of the digital circuit of an adaptive neuro-fuzzy network with on-chip learning capability. The on-chip learning capability was realized by a backpropagation learning circuit for optimizing the network parameters. To maximize the throughput of the circuit and minimize its required resources, we proposed to reuse the computational results in both feedforward and backpropagation circuits. This leads to a simpler data flow and the reduction of resource consumption. To verify the effectiveness of the circuit, we implemented the circuit in an FPGA development board and compared the performance with the neuro-fuzzy system written in a MATLAB code. The experimental results show that the throughput of our neuro-fuzzy circuit significantly outperforms the NF network written in a MATLAB code with a satisfactory learning performance.