Neural Networks (NN), Type-1 Fuzzy Logic Systems (T1FLS) and Interval Type-2 Fuzzy Logic Systems (IT2FLS) are universal approximators, they can approximate any non-linear function. Recent research shows that embedding T1FLS on an NN or embedding IT2FLS on an NN can be very effective for a wide number of non-linear complex systems, especially when handling imperfect information. In this paper we...

As a result from the demanding of process safety, reliability and environmental constraints, a called of fault detection and diagnosis system become more and more important. This statement can be seen from literature covering different aspects and applications implementation. A variety of methods already proposed whether applied single or combined method. One of the feasible ways is by combinin...

This paper presents a novel approach to control the speed of BLDC motor by using PID, Fuzzy, Neural Network and Anti-windup Controllers. The PID controllers is set to optimize the motor parameters such as rise time, peak time, Maximum peak overshoot and Settling time. The fuzzy controller adopts fuzzy logic to retune the PID parameters. Based on the mathematical model of BLDC Motor, novel adapt...

This paper presents the development of recurrent neural network based fuzzy inference system for identification and control of dynamic nonlinear plant. The structure and algorithms of fuzzy system based on recurrent neural network are described. To train unknown parameters of the system the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are forme...

In this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. Utilizing the Generalized characterization Theorem. Then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. Here neural network is considered as a part of large eld called ne...

The speed-traffi c fl ow density interdependence diagram has a number of varia ons, star ng with the theore cal model, through various empirical models that were developed and models based on actual research done on traffi c fl ow. The func onal interdependence is obtained using the Sugeno fuzzy logic system, where representa ve values proposed in HCM 2010 have been adopted as parameters of out...

-Fuzzy ARTMAP is one of the recently proposed neural network paradigm where the fuzzy logic is incorporated. In this paper, we compare the Fuzzy ARTMAP neural network and the well-known back-propagation based Multi-layer perceptron (MLP), in the context of hand-written character recognition problem. The results presented in this paper shows that the Fuzzy ARTMAP out-performs its counterpart, bo...

How to reconfigure a logic gate for a variety of functions is an interesting topic. In this paper, a different method of designing logic gates are proposed. Initially, due to the training ability of the multilayer perceptron neural network, it was used to create a new type of logic and full adder gates. In this method, the perceptron network was trained and then tested. This network was 100% ac...

Aircraft burst fault is uncertainty and ambiguity. Considering QAR data as the research object, the fault diagnosis system based on the T-S fuzzy neural network combined with aircraft maintenance processes is built. First, the system designs the network performance oversight function to improve genetic neural network program. Then the fuzzy logic is used to deal with fuzzy rules, which can dete...