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The inverted pendulum system is a classic control problem that is used in universities around the world. It is a suitable process to test prototype controllers due to its high non-linearities and lack of stability. The inverted pendulum represents a challenging control problem, which continually moves toward an uncontrolled state. This paper presents the possibility of balancing an inverted pendulum system using sliding mode control (SMC). The goal is to determine which control strategy delivers better performance with respect to pendulum’s angle and cart's position. Therefore, proportional-integral-derivative (PID) is used for comparison. Results have proven SMC control produced better response compared to PID control in both normal and noisy systems.

This research presents a handwritten signature recognition based on angle feature vector using Artificial Neural Network (ANN). Each signature image will be represented by an Angle vector. The feature vector will constitute the input to the ANN. The collection of signature images will be divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested for recognition of the signature. When the signature is classified correctly, it is considered correct recognition otherwise it is a failure., {"references":["V. K. Madasu and B. C. Lovell, \"An Automatic Offline Signature \nVerification and Forgery Detection System\", IGI Global, 2008,pp. 63-\n94.W.-K. Chen, Linear Networks and Systems (Book style). Belmont, \nCA: Wadsworth, 1993, pp. 123–135.","O. Elrajubi, \"Off-line Signature Verification Based on Fuzzy Logic \", \nThe Academy of Graduate Studies-Tripoli, Libya, June 2009.","H. Demuth, M. Beale, \"Neural Network Toolbox\", Version 4, September \n2000.","S. A. Hannan, R. R. Manza, R. J. Ramteke, \"Generalized Recognition \nNeural Network and Radial Basis function for Heart Disease Diagnosis\", International Journal of Computer Applications (0975-\n8887), Volume 7-No.13, October 2010.","D. C. Silverman, \"A General Regression Artificial Neural Network\", \nIEEE Transactions on Neural Networks, 2, p. 568, 1991."]}