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4 NEURAL DYNAMICS OPTIMIZATION MODEL Adeli and Park (1998) present a general neural dynamics model for optimization problems that guarantees a stable and local optimum solution. This solved one of the fundamental problems of using ANNs for optimization: How to find a neural dynamics system for a particular optimization problem that would produce a stable and local optimum solution. The neural dynamics optimization model is robust and particularly effective for large and complex optimization problems.
Sayed and Razavi (2000) combine fuzzy logic with an adaptive B-spline network to model the behavioral mode choice in the area of transportation planning. They apply the model to a bimodal example for shipment of commodities (rail and Interstate Commerce Commission-regulated motor carriers for shipments over 500 lb). 3 Wavelets Neural network models can lose their effectiveness when the patterns are very complicated or noisy. Traffic data collected from loop detectors installed in a freeway system and transmitted to a central station present such patterns.
Kasperkiewicz et al. , 1991) to predict strength properties of high-performance concrete mixes as a factor of six components: cement, silica, superplasticizer, water, fine aggregate, and coarse aggregate. Furuta et al. (1996) describe a fuzzy expert system for damage assessment of reinforced concrete bridge decks using genetic algorithms and neural networks. The goal is to automatically acquire fuzzy production rules through the use of the GA and the BP neural networks. The weights of the links obtained from the neural networks are used in the GA evaluation function to obtain the optimal combination of rules to be used in the knowledge base of the expert system (Adeli, 1988; Adeli and Balasubramanyam, 1988; Adeli, 1990a, b).