The choice of hidden non-linearity in a feed-forward multi-layer perceptron (MLP) architecture is crucial to obtain good generalization capability and better performance. Nonetheless, little attention has been paid to this aspect in the ASR field. In this work, we present some initial, yet promising, studies toward improving ASR performance by adopting hidden activation functions that can be au...

This article describes linear and nonlinear Artificial Neural Network(ANN)-based predictors as Autoregressive Moving Average models with Auxiliary input (ARMAX) process for Signal to Interference plus Noise Ratio (SINR) prediction in Direct Sequence Code Division Multiple Access (DS/CDMA) systems. The Multi Layer Perceptron (MLP) neural network with nonlinear function is used as nonlinear neura...

A functional dual adaptive control for slowly time variant nonlinear stochastic multivariable systems is presented. A nonlinear system is modelled by a MultiLayer Perceptron (MLP) neural network. Parameters of the model are estimated by the extended Kalman filter (EKF). Controller design is based on bicriterial dual approach that uses two separate criteria to introduce opposing aspects between ...

background: although experiments on water quality are time consuming and expensive, models are often employed as supplement to simulate water quality. artificial neural network (ann) is an efficient tool in hydrologic studies, yet it cannot predetermine its results in the forms of maps and geo-referenced data. methods: in this study, ann was applied to simulate groundwater quality and geographi...

The objective of this work is to model simulation data a dust devils in Comsol using neuro-fuzzy methods (ANFIS: Adaptive Neuro Fuzzy Inference Systems) and perceptron neural networks. Since the number simulations performed was insufficient, we used Spline function increase amount data. results show that more effective than obtained models are excellent, with Nash -Sutcliffe criterion value abo...

Estimate of sediment load is required in a wide spectrum of water resources engineering problems. The nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods to simulate the suspended sediment load. In this study Artificial Neural Networks (ANNs) are employed to estimate daily suspended sediment load. Two different ANN algorithms, Multi Layer Perce...

Occupying more than 70% of the concrete’s volume, aggregates play a vital role as the raw feed for construction materials; particularly in the production of concrete and concrete products. Often, the characteristics such as shape, size and surface texture of aggregates significantly affect the quality of the construction materials produced. This article discusses a novel method for automatic cl...

Estimating the diameter increment of forests is one most important relationships in forest management and planning. The aim this study was to provide insight into application two machine learning methods, i.e., multilayer perceptron artificial neural network (MLP) adaptive neuro-fuzzy inference system (ANFIS), for developing models Hyrcanian forests. For purpose, diameters at breast height (DBH...

In the Bayer process, the reaction of silica in bauxite with caustic soda causes the loss of great amount of NaOH. In this research, the bound-soda losses in Bayer process solid residue (red mud) are predicted using intelligent techniques. This method, based on the application of regression and artificial neural networks (AAN), has been used to predict red mud bound-soda losses in Iran Alumina C...

Human recognizes speech emotions by extracting features from the speech signals received through the cochlea and later passed the information for processing. In this paper we propose the use of Mel-Frequency Cepstral Coefficient (MFCC) to extract the speech emotion information to provide both the frequency and time domain information for analysis. Since features extracted using the MFCC simulat...