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Artificial Neural Networks Industrial and Control Engineering Applications Part 7

Tham khảo tài liệu 'artificial neural networks industrial and control engineering applications part 7', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Part 3 Food Industry 10 Application of Artificial Neural Networks to Food and Fermentation Technology Madhukar Bhotmange and Pratima Shastri Laxminarayan Institute of Technology Rashtrasant Tukadoji Maharaj Nagpur University Nagpur 440033. India 1. Introduction Every system is controlled by certain parameters and works at its best for a certain combination of the values of these parameters Input parameters of the system are defined as the independent variables or causes which affect the values of output parameters commonly identified as effects. The relationship in many case is typically nonlinear and complex. Different input parameters -apart from their individual influences - may affect the output parameter in synergistic or antagonistic way. The knowledge of cause-and-effect relationships is important in the solution of problems in all fields of endeavor. In the simplest of cases these relationships may take on a linear form while in others highly nonlinear and complex relationships may be appropriate. Some relationships are static while others involve dynamic or time varying elements. A complex system like thermal processing requires maximum destruction of undesirable microorganisms with minimum loss of freshness taste texture and flavor as the outputs with time temperature can size etc. as extrinsic causes along with the composition viscosity and thermal properties of food material as intrinsic causes. Product development happens to be an equally complex system where level and proportion of ingredients are the inputs which determine the sensory parameters cost and marketability. Modeling of bioprocesses for engineering applications is equally challenging task due to their complx nonlinear dynamic behaviour. The conditions of best functioning are called optimum operating functioning conditions. Large number of experiments need to be performed under certain set of conditions for obtaining these optimum parameters. Still the results at selected data points need .