runoff estimation is one of the main challenges encountered in water and watershed management. spatial and temporal changes of factors which influence runoff due to het-erogeneity of the basins explain the complicacy of relations. artificial neural network (ann) is one of the intelligence techniques which is flexible and doesn’t call for any much physically complex processes. these networks can...

In the past few years, artiicial neural networks (ANNs) have been investigated as a tool for time series analysis and forecasting. The most popular architecture is the multilayer perceptron, a feedforward network often trained by back-propagation. The forecasting performance of ANNs relative to traditional methods is still open to question although many experimenters seem optimistic. One proble...

A new classifier algorithm based on Multilayer Perceptron Neural Network (MPNN), Apriori association rules, and Particle Swarm Optimization (PSO) models is proposed. It provides a comprehensive analytic method for establishing an Artificial Neural Network (ANN) with self-organizing architecture by finding an optimal number of hidden layers and their neurons, less number of effective features of...

Supervised Artificial Neural Networks (ANN) are information processing systems that adapt their functionality as a result of exposure to input-output examples. To this end, there exist generic procedures and techniques, known as learning rules. The most widely used in the neural network context rely in derivative information, and are typically associated with the Multilayer Perceptron (MLP). Ot...

This paper reports a comparative study between continuous hidden Markov model (CHMM) and artificial neural network (ANN) on text dependent, closed set speaker identification (SID) system with Thai language recording in office environment. Thai isolated digit 0-9 and their concatenation are used as speaking text. Mel frequency cepstral coefficients (MFCC) are selected as the studied features. Tw...

Forecasting is the starting point for drawing good strategies facing the demand variability in the increasingly complex and competitive today's markets. This article discusses two methods of dealing with demand variability in seasonal time series using artificial neural networks (ANN). First a multilayer perceptron model for time series forecasting is proposed. Several learning rules used ...

This article discusses two methods of dealing with demand variability. First a causal method based on multiple regression and artificial neural networks have been used. The ANN is trained for different structures and the best is retained. Secondly a multilayer perceptron model for time series forecasting is proposed. Several learning rules used to adjust the ANN weights have been evaluated. The...

We devise a feed-forward Artiicial Neural Network (ANN) procedure for predicting utility loads and present the resulting predictions for two test problems given by \The Great Energy Predictor Shootout-The First Building Data Analysis and Prediction Competition" 1]. Key ingredients in our approach are a method (test) for determining relevant inputs and the Multilayer Perceptron. These methods ar...

We devise a feed-forward Artiicial Neural Network (ANN) procedure for predicting utility loads and present the resulting predictions for two test problems given by \The Great Energy Predictor Shootout-The First Building Data Analysis and Prediction Competition" 1]. Key ingredients in our approach are a method (test) for determining relevant inputs and the Multilayer Perceptron. These methods ar...

In order to predict the ice accretion on overhead line conductors, five artificial neural network (ANN) architectures were explored and compared. Two static networks, Multilayer Perceptron and Radial Basis Functions, as well as two time dependent networks, Finite Impulse Response and Elman, were compared with multiple linear regression (ADALINE). Results indicated that the FIR network yielded t...