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ABSTRACT: In spite of great importance of fuzzy neural networks for solving wide range of real-world problems, unfortunately, little progress has been made in their development. In this study we have discussed recurrent neural networks with fuzzy weights and biases as adjustable parameters and internal feedback loops, which allows capturing dynamic response of a system without using external feedback through delays. In this case all the nodes are able to process linguistic information. As the main problem regarding fuzzy and recurrent fuzzy neural networks that limits their application range is the difficulty of proper adjustment of fuzzy weights and biases, we put an emphasize on the RFNN training algorithm. We have proposed the standard DEO-based method for learning of recurrent fuzzy neural network. The optimization method, customized for RFNN training, compares favorably with the existing gradient-based error minimization method as it is less complex and is more likely to locate the global minimum of network error. As the method does not require derivative information, it is very effective in case when dealing with different distance functions. Also, the considered global optimization algorithm can provide high accuracy of fuzzy mapping with relatively smaller network size. The RFNN was tested on a number of benchmark identification and time-series forecasting problems well-known in the literature as well as on application problems. Experimental results demonstrated very good performance on all considered problems.
09/2008; , ISBN: 978-953-7619-08-4
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ABSTRACT: 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 Tend-Tstart results according to the other intelligent battery charger works.
07/2007: pages 307-316;
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ABSTRACT: The paper analyses issues leading to errors in graphic object classifiers. The distance measures suggested in literature and used as a basis in traditional, fuzzy, and Neuro-Fuzzy classifiers are found to be not suitable for classification of non-stylized or fuzzy objects in which the features of classes are much more difficult to recognize because of significant uncertainties in their location and gray-levels. The authors suggest a neuro-fuzzy graphic object classifier with modified distance measure that gives better performance indices than systems based on traditional ordinary and cumulative distance measures. Simulation has shown that the quality of recognition significantly improves when using the suggested method.
Iranian journal of fuzzy systems 01/2004; 1:5-15. · 1.06 Impact Factor
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ABSTRACT: The paper analyses issues leading to errors in graphic object classifiers. The distance measures suggested in literature and used as a basis in Traditional, fuzzy, and Neuro-Fuzzy classifiers are found to be not very suitable for classification of non-stylized or fuzzy objects in which the features of classes are much more difficult to recognize because of significant uncertainties in their location and gray-levels. The authors suggest a Neuro-Fuzzy graphic object classifier with modified distance measure that gives better performance indices than systems based on traditional ordinary and cumulative distance measures. The simulation has shown that the quality of recognition significantly improves when using the suggested method.
Neural Networks, 2003. Proceedings of the International Joint Conference on; 08/2003
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ABSTRACT: Aggregate production–distribution planning (APDP) is one of the most important activities in supply chain management (SCM). When solving the problem of APDP, we are usually faced with uncertain market demands and capacities in production environment, imprecise process times, and other factors introducing inherent uncertainty to the solution. Using deterministic and stochastic models in such conditions may not lead to fully satisfactory results. Using fuzzy models allows us to remove this drawback. It also facilitates the inclusion of expert knowledge. However, the majority of existing fuzzy models deal only with separate aggregate production planning without taking into account the interrelated nature of production and distribution systems. This limited approach often leads to inadequate results. An integration of the two interconnected processes within a single production–distribution model would allow better planning and management. Due to the need for a joint general strategic plan for production and distribution and vague planning data, in this paper we develop a fuzzy integrated multi-period and multi-product production and distribution model in supply chain. The model is formulated in terms of fuzzy programming and the solution is provided by genetic optimization (genetic algorithm). The use of the interactive aggregate production–distribution planning procedure developed on the basis of the proposed fuzzy integrated model with fuzzy objective function and soft constraints allows sound trade-off between the maximization of profit and fillrate. The experimental results demonstrate high efficiency of the proposed method.
Information Sciences.
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ABSTRACT: Fuzzy neural networks (FNN) as opposed to neuro-fuzzy systems, whose main task is to process numerical relationships, can process both numerical (measurement based) information and perception based information. In spite of great importance of fuzzy feed-forward and recurrent neural networks for solving wide range of real-world problems, today there are no effective training algorithm for them. Currently there are two approaches for training of FNN. First approach is based on application of the level-sets of fuzzy numbers and the back-propagation (BP) algorithm. The second approach involves using evolutionary algorithms to minimize error function and determine the fuzzy connection weights and biases. The method based on the second approach was proposed by the authors and published in Part 1 of this paper [R.A. Aliev, B. Fazlollahi, R. Vahidov, Genetic algorithm-based learning of fuzzy neural networks. Part 1: feed-forward fuzzy neural networks, Fuzzy Sets and Systems 118 (2001) 351–358]. In contrast to the BP and other supervised learning algorithms, evolutionary algorithms do not require nor use information about differentials, and hence, they are most effective in case where the derivative is very difficult to obtain or even unavailable. However, the main deficiency of the existing FNN based on the feed-forward architecture is its adherence to static problems. In case of dynamic or temporal problems there is a need for recurrent fuzzy neural networks (RFNN). Designing efficient training algorithms for RFNN has recently become an active research direction. In this paper we propose an effective differential evolution optimization (DEO) based learning algorithm for RFNN with fuzzy inputs, fuzzy weights and biases, and fuzzy outputs. The effectiveness of the proposed method is illustrated through simulation of benchmark forecasting and identification problems and comparisons with the existing methods. The suggested approach has also been used for real applications in an oil refinery plant for petrol production forecasting.
Fuzzy Sets and Systems.
