In an earlier companion paper [56] a supervised learning neural network pattern classifier called the fuzzy min-max classification neural network was described. In this sequel, the unsupervised learning pattern clustering sibling called the fuzzy min-max clustering neural network is presented. Pattern clusters are implemented here as fuzzy sets using a membership function with a hyperbox core t...

Distributed flexible AC- transmission system (D-FACTS) is a recently advanced FACTS device with high flexibility and smaller size. The DPFC can control power flow in transmission lines, regulate bus voltages and it can also enhance stability margin in power grids. Adaptive-neural network-based fuzzy inference system (ANFIS) combines features of artificial neural network and fuzzy controller. Th...

The defects of BP neural network, such as low convergence speed, falling into local minimum easily, bad generalization ability, can depress the calculation accuracy of BP neural network and damage its practical effect. So the research of improving BP neural network has great theoretical and practical significance. The paper advances a new fuzzy neural network algorithm to overcome the defects o...

This paper investigates some properties of Takagi-Sugeno (T-S) fuzzy Hopfield neural networks. First, we prove that there exists a unique solution of the T-S fuzzy Hopfield neural network. Second, we determine a condition for input-to-state stability (ISS) of the T-S fuzzy Hopfield neural network. These results will be useful to analyze dynamic behavior of fuzzy neural networks.

In this paper statistical and time series models are used for determining the random drift of a dynamically Tuned Gyroscope (DTG). This drift is compensated with optimal predictive transfer function. Also nonlinear neural-network and fuzzy-neural models are investigated for prediction and compensation of the random drift. Finally the different models are compared together and their advantages a...

This paper proposes a fuzzy inference based neural network for the forecasting of short term loads. The forecasting model is the integration of fuzzy inference engine and the neural network, known as Fuzzy Inference Neural Network (FINN). A FINN initially creates a rule base from existing historical load data. The parameters of the rule base are then tuned through a training process, so that th...

unknown noise and artifacts present in medical signals with  non-linear fuzzy filter will be estimate and then removed. an adaptive neuro-fuzzy interference system which has a nonlinear  structure presented  for the noise function prediction by before samples. this paper is about a neuro-fuzzy method to estimate unknown noise of electrocardiogram (ecg) signal. adaptive neural combined with fuzz...

suitable soil structure is important for crop growth. one of the main characteristics of soil structure is the size of soil aggregates. there are several ways of showing the stability of soil aggregates, among which the determination of the median weight diameter of soil aggregates is the most common method. in this paper, a method based on adaptive neuro fuzzy inference system (anfis) was used...

In neural network the connection strength of each neuron is updated through learning. Through repeated simulations of crisp neural network, we propose the idea that for each neuron in the network, we can obtain reduced model with more efficiency using wavelet based multiresolution analysis (MRA) to form wavelet based quasi fuzzy weight sets (WBQFWS). Such type of WBQFWS provides good initial so...