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...

distinguishing productive zones of a drilled oil well plays a very important role for petroleum engineers to decide where to perforate to produce oil. conventionally, net pay zones are determined by applying a set of cut-offs on perophysical logs. as a result, the conventional method finds productive intervals crisply. in this investigation, a net index value is proposed, then; diffusivity equa...

in this paper, a novel hybrid method based on learning algorithmof fuzzy neural network and newton-cotesmethods with positive coefficient for the solution of linear fredholm integro-differential equation of the second kindwith fuzzy initial value is presented. here neural network isconsidered as a part of large field called neural computing orsoft computing. we propose alearning algorithm from ...

We propose a constructive method, inspired by Simpson’s Min-Max technique, for obtaining fuzzy neural networks. It adopts a cost function depending on a unique net parameter. This feature allows us to apply a simple unimodal search for determining this parameter and hence the architecture of the optimal net. The algorithm shows a good behavior with respect to other methods when applied to real ...

A new more efficient variant of a recently developed algorithm for unsupervised fuzzy clustering is introduced. A Weighted Incremental Neural Network (WINN) is introduced and used for this purpose. The new approach is called FC-WINN (Fuzzy Clustering using WINN). The WINN algorithm produces a net of nodes connected by edges, which reflects and preserves the topology of the input data set. Addit...

In this paper we investigate the use of a supervised, but self-organizing, Adaptive Resonance Theory type of neural network (Fuzzy-ARTMAP), for first break picking in seismic reflection data. First break picking is the accurate location of the leading energy pulse received by a geophone in response to a seismic shot. The performance of Fuzzy-ARTMAP is compared to our previous work with multi-la...

This paper presents an adaptive neuro-controller for intelligent control of non-linear dynamical system. The formed as the fuzzy selective neural net the adaptive neuro-controller on the basе of system’s state, creates the effective control signal under random perturbations. The validity and advantages of the proposed adaptive neuro-controller are demonstrated by numerical simulations. The simu...

Shao-Han Liu Jzau-Sheng Lin, MEMBER SPIE National Chin-Yi Institute of Technology Department of Electronic Engineering No. 35, Lane 215, Sec. 1, Chung-Shan Rd Taiping, Taichung, Taiwan E-mail: [email protected] Abstract. A new fuzzy Hopfield-model net based on rough-set reasoning is proposed for the classification of multispectral images. The main purpose is to embed a rough-set learning...