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I. INTRODUCTION. Artificial neural network (ANN) model have an ability to recognize time series patterns and nonlinear optimization and training algorithm optimization (Hagan et al., 1996). As shown in Neural Network Design. 1st Edn. De Jes s NEURAL NETWORK DESIGN (2nd Edition) provides a clear and detailed A free 1012 page eBook version of the book (11.4(More)
This work presents the implementation of trainable Artificial Neural Network (ANN) chip, which can be trained to implement certain functions. Usually training of neural networks is done off-line using software tools in the computer system. The neural networks trained off-line are fixed and lack the flexibility of getting trained during usage. In order to(More)
This paper a survey of artificial neural network on various welding technology and also discusses the result based on analysis. The role of optimization techniques in concerned domain with experimental analysis is explained. Refer ences-Parikshik dutta, Dilip Kumar pratihar do modelling of TIF welding process using conventional regression analysis and(More)
AC servo systems are extensively used in robotic actuators and are competing with DC servo motors for motion control because of their favorable electrical and mechanical properties. This paper presents an approach towards the control system tuning for the speed control of an AC servo motor. An approach towards speed control of servo motor in presence of(More)
Stock market prediction plays a vital rule in taking financial decisions. Various factors affecting the stock market makes stock prediction somewhat complex and difficult. Different data mining techniques such as Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) etc are being widely used for predicting stock prices of different(More)