This paper presents a novel approach for training and output prediction of data of a Linear variable differential transformer (LVDT). LVDT is a commonly used device used in laboratories for measuring linear displacements in specific situations. This article considers application of Artificial Neural Networks (ANNs) for learning and output estimation of LVDT. Real-time experiments were conducted and results were collected for training of ANNs. The Regression results and outputs verified the learning and prediction capability of ANNs.

This paper illustrates a comparison study for control of highly non-linear Double Inverted Pendulum (DIP) on cart. A Matlab-Simulink model of DIP has been built using Newton's second law. The Neuro-fuzzy controllers stabilizes pendulums at vertical position while cart moves in horizontal direction. This study proposes two soft-computing techniques namely Fuzzy logic reasoning and Neural networks (NN's) for control of DIP systems. The results shows that Fuzzy controllers provides better results as compared to NN's controllers in terms of settling time (sec), maximum overshoot (degree) and steady state error. The regression (R) and mean square error (MSE) values obtained after training of Neural network were satisfactory. The simulation results proves the validity of proposed techniques.

This paper presents a comparison study of different control strategies for stabilizing highly non-linear Gantry inverted pendulum (GIP) system. The control objective was achieved using three different soft-computing techniques i.e. Fuzzy logic (FL), Adaptive neuro fuzzy inference system (ANFIS) and Neural networks (NN's). The results obtained from fuzzy controller were further optimized using ANFIS and NN's controllers. The performance parameters considered for analysis were Settling time (seconds), Maximum Overshoot (degree) and Steady state error. The simulation results that both fuzzy and ANFIS controllers were able to stabilize the non-linear GIP system within specified time. It was also observed that ANFIS controller shows better learning ability as compared to NN's controller. The study also elaborates the relationship between Membership functions (MF's) and training error tolerance for ANFIS controller and relation between hidden neurons and Mean squared error (MSE) and Regression (R) value for NN's controller.

EDM is a non-contact machining process widely used for shaping electro-conductive materials regardless of their hardness. In EDM material removal takes place by a series of recurring electrical sparks between the tool electrode and workpiece. In this study the effect of variation of discharge current on various machining parameters including Metal removal rate (MRR), Tool removal rate (TRR) and Surface roughness has been considered. A total of 32 experiments were conducted on four different workpieces i.e. Die Steel-D3, En-8, En-19 and Stainless steel (SS-AISI-440C) with the help of Copper and Aluminium electrodes. In this study Die-Sinking EDM has been employed and the results are shown with the help of graphs.