The fuzzy flip-flop based multilayer perceptron, named Fuzzy Neural Network, FNN is proposed for function approximation. In recent years much effort has been made for the development of a special kind of bacterial memetic algorithm for optimization and training of the fuzzy neural network parameters. In this approach the FNN parameters have been encoded in a chromosome and participate in the ba...

Options pricing model parameters are inherently imprecise due to fluctuations in the real-world financial market. Traditional option pricing methods do not account for the uncertainty in parameters, but the fuzzy set theory may be applicable. This paper proposes a cash-or-nothing European call binary option pricing model based on the hypothesis that the underlying asset price, risk-free rate of...

Medical images in the process of collection and transmission are susceptible to noise interference and become blurred. The parameters of traditional fuzzy enhancement algorithm are controlled by manual adjustment, often with poor efficiency and effect, which cannot be optimal. Because a fruit fly optimization algorithm has small number of parameters with global optimization capability, in this ...

A self-organizing neural network is proposed which is inherently a fuzzy inference system with the capability of learning fuzzy rules from data. The learning strategy consists of two phases: a self-organizing clustering to establish the structure of the network as well as the initial values of its parameters and a supervised learning phase for optimal adjustment of these parameters. After learn...

The prediction of disorder is difficult than prediction of disease and Autism is one of the mental disorder which affect the person social life and communication. social and communication are important factor in life. This paper presented a neuro-fuzzy technique that is used to predict patient autism level based on various parameters like social levels and communications and emotional and behav...

An improved self-tuning mechanism of fuzzy control by gradient descent method is presented. The membership function parameters are tuned by minimizing some criterion defined on the control output using the steepest gradient descent algorithm. The factor which controls how much the fuzzy controller parameters are altered is adjusted continuously using a set of fuzzy rules. This varying factor is...

we use the basic binomial option pricing method but allow someor all the parameters in the model to be uncertain and model this uncertaintyusing fuzzy numbers. we show that with the fuzzy model we can, with areasonably small number of steps, consider almost all possible future stockprices; whereas the crisp model can consider only n + 1 prices after n steps.

In conventional shortest path problems, it is assumed that decision maker is certain about the parameters (distance, time etc.) between different nodes. But in real life situations, there always exist uncertainty about the parameters between different nodes. In such cases, the parameters are represented by fuzzy numbers. In this paper the shortcomings of the existing algorithm are pointed out a...

Fuzzy numbers, and more generally linguistic values, are approximate assessments, given by experts and accepted by decision-makers when obtaining more accurate values is impossible or unnecessary. To simplify the task of representing and handling fuzzy numbers, several authors have introduced real indices in order to capture the information contained in a fuzzy number. In this paper we propose ...

Fuzzy controllers are designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. The optimization of these parameters can be carried out by neural networks, which are designed to learn from training data, but which a...