In this study, artificial neural network was used to predict the microhardness of Al2024-multiwall carbon nanotube(MWCNT) composite prepared by mechanical alloying. Accordingly, the operational condition, i.e., the amount of reinforcement, ball to powder weight ratio, compaction pressure, milling time, time and temperature of sintering as well as vial speed were selected as independent input an...

Simultaneous spectrophotometric estimation of Fluoxetine and Sertraline in tablets were performed using UV–Vis spectroscopic and Artificial Neural Networks (ANN). Absorption spectra of two components were recorded in 200–300 (nm) wavelengths region with an interval of 1 nm. The calibration models were thoroughly evaluated at several concentration levels using the spectra of synthetic binary mix...

The present investigation deals with the study and development of a surface roughness prediction model for the machining of unidirectional glass fiber reinforced plastics (UDGFRP) composite using Artificial Neural Network (ANN). The feed forward back propagation is used. Taguchi method (Orthogonal L16 array) is employed to carry out the experimental work. The process parameters selected for stu...

This study high lights on the subject of weight initialization in back-propagation feed-forward networks. Training data is analyzed and the notion of critical points is introduced for determining the initial weights and the number of hidden units. The proposed method has been applied to arti cial data and the publicly available cancer database. The experimental outcomes indicate that the propos...

A speaker independent bimodal phonetic classification experiment regarding the Italian plosive consonants is described. The phonetic classification scheme is based on a feed forward recurrent back-propagation neural network working on audio and visual information. The speech signal is processed by an auditory model producing spectral-like parameters, while the visual signal is processed by a sp...

A phonetic classification scheme based on a feed forward recurrent back-propagation neural network working on audio and visual information is described. The speech signal is processed by an auditory model producing spectral-like parameters, while the visual signal is processed by a specialised hardware, called ELITE, computing lip and jaw kinematics parameters. Some results will be given for va...

We propose a general class of multilayer feed-forward neural networks where the combination of inputs in every node is formed by hybrid linear and nonlinear (of the morphological/rank type) operations. For its design we formulate a methodology using ideas from the back-propagation algorithm and robust techniques to circumvent the non-diierentiability of rank functions. Experimental results in a...

In this paper, we adapt the classical learning algorithm for feed-forward neural networks when monotonicity is required in the inputoutput mapping. Monotonicity can be imposed by adding of suitable penalization terms to the error function. This yields a computationally efficient algorithm with little overhead compared to back-propagation. This algorithm is used to train neural networks for dela...