Kaijun Xu

An Application of Dual-fuzzy Neural-networks to Design of Adaptive Fuzzy Controllers

This paper presents a design method of adaptive fuzzy controllers using dual-fuzzy neural networks (DFNNs). For the convenience of adaptive control, the structure of the two-fuzzy neural-network controller is divided into two parts. Each part is a fuzzy neural-network (FNN). The adaptive controller uses two FNNs. One FNN is used to identify a fuzzy model of controlled object. The fuzzy control rule is designed with the linguistic rules of the fuzzy model and is incorporated into the other FNN. Simulations using a controlled object with an non-monotonic input-output characteristics are done to verify the proposed design method.

A Kind of Resolution Method of Linguistic Truth-Valued Propositional Logic Based on LIA

In the present paper, based on lattice implication algebra, a kind of linguistic truth-valued propositional logic is constructed with some logic properties regarding reasoning being provided which can express both the comparable and incomparable truth values. Secondly, the satisfiability problem is discussed. J-true, J-false of a formula, J-similar literals and J-complementary literals are proposed based on a filter J of L. Finally, Based on the degree of complementary and degree of similar J-resolution method in the filter of the linguistic truth-valued propositional logic is introduced.

A Dynamic Neuro-Fuzzy Controller for Gas-Fired Water Heater

This paper presents the dynamic neuro-fuzzy control system for controlling a gas-fired water heater. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic neural networks in the forward path. A dynamic identification network (DIN) is used to identify the output of the manipulator system, and a dynamic learning network (DLN) is employed to learn the weighting factor of the fuzzy logic. It is envisaged that the integration of fuzzy logic and neural network based-controller will encompass the merits of both technologies, and thus provide a robust controller for the gas-fired water heater system. The fuzzy logic controller, based on fuzzy set theory, provides a means for converting a linguistic control strategy into control action and offering a high level of computation. On the other hand, the ability of a dynamic network structure to model an arbitrary dynamic nonlinear system is incorporated to approximate the unknown nonlinear input-output relationship using a dynamic back propagation learning algorithm.