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...

This paper is an attempt to assess the potential and usefulness of ANN based modeling for evaporation prediction from a reservoir, where in classical and empirical equations failed to predict the evaporation accurately. The meteorological data set of daily pan evaporation, temperature, solar radiation, relative humidity, wind speed is used in this study. The performance of feed forward back pro...

In this paper, we show how Bayesian neural networks can be used for time series analysis. We consider a block based model building strategy to model linear and nonlinear features within the time series. A proposed model is a linear combination of a linear autoregression term and a feedforward neural network (FFNN) with an unknown number of hidden nodes. To allow for simpler models, we also cons...

The purpose of this study is to analyze the performance of Back propagation algorithm with changing training patterns and the second momentum term in feed forward neural networks. This analysis is conducted on 250 different words of three small letters from the English alphabet. These words are presented to two vertical segmentation programs which are designed in MATLAB and based on portions (1...

The conceptual high-level design of a low cost neural network (NN) based autonomous vehicle is presented. Focus is on the domain of indoor as well as small outdoor applications such as mail delivery system among neighboring organizations. The design is based on four independent neural networks attached with ultrasonic rangefinders; Inertial Measuring Unit (IMU) and GPS receiver. The NN model is...

Facial expression recognition is also gaining interest among the researchers because of its inevitable advantages in image retrieval which can be extended many fields like medicine, artificial intelligence, robotics and neural networks. So it is one of the hot topic for researchers. Existing methods such as PCA, LDA, LPP etc. with Euclidian distance classifier are popular. Neural network classi...

This paper introduces a novel two-stream deep model based on graph convolutional network (GCN) architecture and feed-forward neural networks (FFNN) for learning the solution of nonlinear partial differential equations (PDEs). The aims at incorporating both grid input representations using two streams corresponding to GCN FFNN models, respectively. Each stream layer receives processes its repres...

Development of medical domain application has been one of the most active research areas. Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. Model selection for a neural network includes various factors such as selection of the optimal number of hidden nodes, selection of the relevant input variables and selection of optimal c...

The dynamics of the Autonomous Underwater Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate accurately because of the variations of these coefficients with different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain the coupled nonlinear dyna...

in this work, response surface methodology (rsm) and artificial neural network (ann) were used to predict the decolorization efficiency of reactive red 33 (rr 33) by o3/uv process in a bubble column reactor. the effects of four independent variables including time (20-60 min), superficial gas velocity (0.06-0.18 cm/s), initial concentration of dye (50-150 ppm) and ph (3-11) were investigated us...