In this paper, a new architecture of fuzzy neural network (FNN) model is proposed in order to find a fuzzy solution of a fully fuzzy polynomial (FFP) with degree one. The proposed FNN is a two layer feed-forward neural network, that corresponds connection weight to output layer. The proposed architecture of artificial neural network can get a fuzzy input signal and calculates its corresponding ...

This paper proposes an adaptive technique in the prediction of dichotomous response variable by combining fuzzy concept with statistical logistic regression. The model was tested on cancer dataset in predicting cancer susceptibility. In this paper we will present the development, evaluation and validation of the proposed model based on the experiment carried out. Explanatory power of the adapti...

In this paper, we present a neuro-fuzzy model for intelligent reactive power control and efficient utilization of power. The proposed neuro-fuzzy model will assist the conventional power control systems with added intelligence. For on-line control, voltage and current are fed into the network after preprocessing and standardization. The model was trained with a 24-hour load demand pattern and p...

The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy archi...

We propose a fuzzy-neural modeling approach for automatically constructing a fuzzy-neural model from a set of input-output data. The proposed approach consists of two phases, structure identification and parameter identification. In the structure identification phase, rough TSK fuzzy rules are extracted through a clustering algorithm. Then a fuzzy neural network is built in the parameter identi...

It has limitations to apply the traditional mathematical model to assess the risk of the information security for it is characterized by its nonlinearity and uncertainty. The RBF Neural Networks Theory, Particle Swarm Optimization (PSO) Analysis and Fuzzy Evaluation are combined to build a particle swarm optimizing model of Information Security Risk Assessment based on RBF Neural Networks, so a...

The forecast of fluctuations and prices is the major concern in financial markets. Thus, developing an accurate and robust forecasting decision model is critically favorable to the investors. As gold has shown a special capability to smooth inflation fluctuations, governors use gold as a price controlling lever. Thus, more information about future gold price trends will help to make the firm de...

The paper presents the design of municipal creditworthiness parameters. Further, the design of model for municipal creditworthiness classification is presented. The realized data pre-processing makes the suitable economic interpretation of results possible. Municipalities are assigned to clusters by unsupervised methods. The combination of Kohonen’s self-organizing feature maps and K-means algo...

the hybrid fuzzy differential equations have a wide range of applications in science and engineering. we consider the problem of nding their numerical solutions by using a novel hybrid method based on fuzzy neural network. here neural network is considered as a part of large eld called neural computing or soft computing. the proposed algorithm is illustrated by numerical examples and the resu...