Rough Set Theory And Rough Hybrid Intelligent Methods Applied Research, In The Ship Power System

At present, automatic and intelligent control is the important developing direction for modern ships. The electrical capacity of ship generators is growing large, which impels ship power system more and more complex. So the demand for the ship power system research is becoming much higher. It will have the practical significance that adopting the new artificial intelligence technologies and methods to realize information process, dynamic modeling and intelligent control in the ships power system.Rough set theory putted forward by Polish scientist Z. Pawlak is a new soft computing method. It can deal with imprecise, uncertain and incomplete data validly, find the underlying knowledge or rules from initial information system, and can overcome weaknesses of other soft computing method in some aspects. The development of rough set theory and the research of integrating rough set with neural networks provide a new way for ship power station modeling and control. Up to now, the research and application of rough set theory in ship power system are very rare. In this dissertation, according to the data analysis ability of rough set theory in intelligent information processing, and the self-organizing and fault-tolerance capability of neural networks, the dynamic modeling and control for ship power system based on the hybrid intelligent method of rough set theory and RBF neural networks are researched mainly. The major innovations in this article are as follows:Rough Set Theory and AlgorithmsIt is a pity that rough set theory can only deal with the discrete attributes. Aiming at the weaknesses of traditional discretization methods, an algorithm for continuous attributes discretization based on particle swarm optimization (PSO) is presented here, which can reduce the incompatibility degree of discretized decision table. The rough set theory is deeply investigated, and some useful properties of the positive region are discovered. A method for attribute core computation directly based on the positive region is proposed, and then, two algorithms for attribute relative reduction based on positive region are given. In order to leave out the repeated computation of positive region in the attribute reduction process, a kind of generalized information table is introduced; on the basis of it a criterion of attribute core and relative reduction is provided. A method for calculating attribute core is presented. And then, an algorithm for relative attribute reduction based on the generalized information table is designed, which is suitable for not only consistent decision table but also inconsistent decision table. In real applications, many information systems are incomplete because of different reasons, and need pretreatment. An algorithm for calculating attribute relative reduction in incomplete systems directly is put forward. Moreover, the decision rules reduction is an important topic in the research on intelligent information processing based on rough set theory. A method for calculating decision rules core based on binary discernibility matrix directly is presented. And then, an algorithm for decision rules reduction is designed.RBF Neural Networks Constructing based on Rough Set Theory (RS-RBF Neural Networks)Rough set theory and neural networks, which are both valid methods for intelligent information processing, have respective limitations; meanwhile there are fine complementarities between them, which provide the theoretical foundation for their integration research. After analyzing the characteristics of radial basis function and the structure of RBF neural networks deeply, a RBF neural network configuring method based on rough set theory, namely RS-RBF networks, is presented. Firstly, in order to build RBF neuron center vectors candidate set and spreads, the training samples are reduced based on rough set theory. Then, orthogonal least squares method is used to construct RBF networks.Ship Synchronous Generator Dynamic Modeling Method Based on RS-RBF Neural NetworksThe integration of rough set theory and neural networks supplies a powerful way for information processing of complex nonlinear system with uncertain and incomplete data, and also provides a new approach for complex nonlinear system modeling. By virtue of respective advantage about rough set theory and RBF networks as well as their complementarities, the integration of them was researched, and a dynamic modeling method based on RS-RBF neural networks is presented. The method was applied to model the ship synchronous generator with complex dynamic characteristics and uncertainties. The simulation results prove the validity of this method.Ship Generator Excitation Control based on Hybrid Intelligent Rough Control MethodsRough control is a newly arisen intelligent control method in recent years. It is good for rough set theory development in intelligent control field that the particular way of dealing with uncertain problems and the good properties of fusion with other uncertain theories. Although there have been some researches on rough control, their number and domains are relatively small, and they are all academic rather than real life applications. In the field of computational intelligence, it is one of the most promising ways that synthesizing different theories to solve the practical problems. In this dissertation, hybrid intelligent methods based on rough set theory were pilot studied for excitation control system of ship synchronous generator. Aiming at the characteristics of ship power system, two hybrid intelligent rough control methods were presented for the first time. The one is an adaptive neural PID control strategy based on RS-RBF neural networks identification; another one is excitation compound control method with rough neural inverse system feed forward compensation. The simulation results prove the validity of these methods.