Abstract
A predictive method based on Artificial networks has been developed for the thermophysical properties of binary liquid mixtures of diacetone alcohol with benzene, chlorobenzene and bromobenzene at (303.15,313.15 and 323.15) K. In method 1, a committee ANN was trained using 5 physical properties combined with absolute temperature as its input to predict thermo physical properties of liquid mixtures. Using these data we found out the predicted data for intermediate mole fraction of different systems without conducting experiments. ANN with back-propagation algorithm is proposed, for Multi-pass Turning Operation and developed in MATLAB. Compared to other prediction techniques, the proposed ANN approach is highly accurate and error is
Highlights
A Neural Network is an interconnected assembly of simple processing elements, units or nodes, whose functionality is loosely based on the animal neuron
Experimental thermo physical property values were extracted from the data base for all the mixtures studied
The data for prediction is presented to the neural network after training
Summary
A Neural Network is an interconnected assembly of simple processing elements, units or nodes, whose functionality is loosely based on the animal neuron. The processing ability of the network is stored in the inter-unit connection strengths, or weights, obtained by a process of adaptation to, or learning from, a set of training patterns. It has been shown that non linear feed forward neural networks are capable of universal functional approximation and that a single hidden layer is sufficient to uniformly approximate any continuous function Hornic.et al(1989). The neurons in a single hidden layer tends to interact globally but in complex functions this interaction makes it difficult to improve the approximation Heykin (1994), Maren .et al (1990). The brain is principally composed of a very large number (circa 10,000,000,000) of neurons, massively interconnected (with an average of several thousand interconnects per neuron, this varies enormously)
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