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A direct adaptive control system for a class of unknown nonaffine discrete-time plants is introduced in this article. The proposed control law is constructed by the estimated system linearization with adjustable networks called muti-input fuzzy rules emulated networks or MIFRENs. Only on-line learning phase, the bounded parameters inside MIFRENs and the boundary of control error are given by the proposed...
Congestion Control is concerned with allocating the network resources such that the network can operate at an optimum performance level when the demand exceeds or it is near the capacity of the network resources. This paper presents a novel scheme of adaptive Neuro-Fuzzy Inference Controller (ANFIS). The advantages of both Fuzzy Logic and Neural Networks are combined together to design the ANFIS....
In the coal mining production, the fire catastrophe is very dangerous to mining worker life and the whole coal well when the gas explosion is happening. So it is very important to predict fire happening and emit the alarm. In this paper, a novel coal gas fire prediction system is proposed based on multi-sensor data fusion theory. The four different kinds of sensor parameters of the temperature, CO...
In order to realization electronic parts product appearance quality detection control, one kind of processor based on the intelligent knowledge automatic extraction and system intelligence modeling was presented. In the processor, wavelet-fuzzy technique and neural network technique are combined. Uses the fuzzy wavelet extraction image feature, and wavelet function is used as fuzzy membership function...
An anticipatory neuro-fuzzy controller has been developed which combines traditional fuzzy logic with feedback concepts and a neural network to predict system behavior. Fuzzy rules in the system are 'nested' so that it is not necessary to evaluate all rules at each time step if some smaller subset of rules will produce acceptable system behavior. Anticipatory neuro-fuzzy control does not require that...
The previous models of artificial neural networks for control do not use the existing knowledge of a physical system's behavior and train the network from scratch. The learning process is usually long, and even after the learing is completed, the resulting network can not be easily explained. On the other hand, approximate reasoning-based controllers provide a clear understanding of the control strategy...
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