Learn More
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)
Real-world applications will inevitably entail divergence between samples on which chemometric classifiers are trained and the unknowns requiring classification. This has long been recognized, but there is a shortage of empirical studies on which classifiers perform best in 'external validation' (EV), where the unknown samples are subject to sources of(More)