| In the field of intelligent control, there are two kinds of core technology: intelligent information integration and intelligent control algorithms, which construct intelligent control systems. Multi-agent oriented intelligent information integration and neural network oriented control algorithms are studied in this paper. On the one hand, theory methods and architecture are proposed for integration of information in distributed intelligent control systems. On the other hand, by combining fuzzy logic, evolutionary computation and stochastic optimal methods, structure and learning algorithms of neural networks are studied for neural control, which is one of the important branches of intelligent control.Chapter 1, based on a survey on intelligent control, neural networks, distributed artificial intelligence and multi-agent systems, presents the study tasks of this paper.Chapter 2 focuses on weight learning and structure learning of multi-layer feed-forward neural networks. The genetic BP algorithm are discussed. Then two novel algorithms, samples division based heuristic genetic BP and composition structure learning, are proposed. Simulations are performed respectively.Chapter 3 studies structure and learning algorithms of recurrent neural networks. Based on simulated annealing and alopex learning, a novel leaning algorithm, fuzzy logic alopex learning (FLA), is proposed and simulated.A new recurrent neural network structure, self-feedback diagonal recurrent neural networks (SDRNN), is also designed in this chapter. The learning algorithm of SDRNN is given and the convergence of this algorithm is proved. The simulation results show the validation of the structure and the learning algorithm.Chapter 4 deals with neural network based identification and control. After the analysis of several important aspects of neural control, two novel neural controllers are then proposed. The first controller FAM neural controller (FAMNC), based on Pre-defuzzifing FAM, is presented for bridging the gap between FAM and NN. The equivalence of pre-defuzzifing and general FAM is proved constructively. The simulation research on inverted pendulum control is performed. The simulation results show that the FAMNC is sound. The second controller is SDRNN based neural controller. The control architecture is given and the convergence of the control algorithm is proven. The simulation research on a non-BIBO dynamic plant control is performed. The simulation results show the availability of this controller.Chapter 5 turns to distributed artificial intelligence, intelligent agent and multi-agent. First, the concepts, structure categories, research content and application of intelligent agent are introduced. Several formulation of mental states of intelligent agent are discussed. Second, based on former logic theories, a improved formal model, MASCL, is proposed in this chapter. MASCL is a many-sorted first-order branching-time BDI logic, which can capture the requirements for representation mental states, acts, plans and social laws of multi-agent systems. With the use of MASCL, the concept of cooperation commitment of cooperation process in multi-agent systems is defined, and the formal model of cooperation commitment is proposed.Chapter 6 researches into the architecture, solving process and implementation of intelligent agent andmulti-agent systems, and the application to industry process control. First, the significance and characteristics of the application of multi-agent based distributed intelligent control to industry control are discussed. Second, a concept model of intelligent agent is proposed, the architecture and solving process are designed and the information and acts of intelligent agent are described by object-oriented code. Third, a multi-agent modeling and controlling framework are designed for agglomeration process. An experiment system is developed for intelligent control and multi-agent systems studies, and tests are performed on this system.The conclusions of this paper are drawn and the futur...
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