This paper presents the development and comparison of muscle models based on Functional Electrical Stimulation (FES) stimulation parameters using the Nonlinear Auto-Regressive model with Exogenous Inputs (NARX) using Multi-Layer Perceptron and Cascade Forward Neural Network (CFNN). FES stimulations with varying frequency, pulse width and pulse duration were used to estimate the muscle torque. About 722 data points were used to create muscle model. One Step Ahead (OSA) prediction, correlation tests and residual histogram analysis were performed to validate the model. The optimal Multi-Layer Perceptron (MLP) results were obtained from input lag space of 1, output lag space of 43 and hidden units 30. The MLP selected a total of three terms were selected to construct the final model, which producing a final Mean Square Error (MSE) of 1.1299. The optimal CFNN results were obtained from input lag space of 1, output lag space of 5 and hidden units 20 with similar terms selected. The final MSE produced was 1.0320. The proposed approach managed to approximate the behavior of the system well with unbiased residuals, which CFNN showing 8.66% MSE improvement over MLP with 33.33% less hidden units.

No abstract

No abstract

No abstract

Air Quality Index (AQI) system lays an important role in conveying to both decision-makers and the general public the status of ambient air quality, ranging from good to hazardous. Five types of air pollutants will be studied which consists of ozone (O 3 ), carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulphur dioxide (SO 2 ) and suspended particulate matter less than 10 micron in size (PM 10 ). The objective of this paper were to investigate the effectiveness of Artificial Neural Network (ANN) model with Back Propagation Neural Network (BPNN) for predicting the ambient air quality for air quality monitoring in states of Malaysia. The measurement activities are carried at Jalan Tasek in Perak, Nilai in Negeri Sembilan and Jerantut in Pahang. The data collected comprises of data for the previous two months, beginning from November 2004. The ambient air quality plays an important role in evaluating the air quality. The artificial neural network simplifies and speeds up the computation of the ambient air quality, as compared to the currently existing method. For this purposes, neural network model provides an interesting alternative to air quality monitoring. The comparison between data from model predictions and actual observations is coherent which shows that promising result based on the developed ANN model in predicting Ambient Air Quality (AAQ) is effective and accurate.