Download Fractional Dynamics and Control by Md. Mehedi Hasan, X. W. Tangpong, O. P. Agrawal (auth.), PDF

By Md. Mehedi Hasan, X. W. Tangpong, O. P. Agrawal (auth.), Dumitru Baleanu, José António Tenreiro Machado, Albert C. J. Luo (eds.)

Fractional Dynamics and Control offers a complete evaluate of contemporary advances within the components of nonlinear dynamics, vibration and keep an eye on with analytical, numerical, and experimental effects. This e-book presents an summary of contemporary discoveries in fractional keep watch over, delves into fractional variational rules and differential equations, and applies complex recommendations in fractional calculus to fixing advanced mathematical and actual problems.Finally, this booklet additionally discusses the position that fractional order modeling can play in complicated platforms for engineering and science.

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Das S (2008) Functional fractional calculus for system identification and controls, 1st edn. Springer, New York 2. Oldham KB, Spanier J (1974) The fractional calculus. Academic, London 3. Podlubny I (1998) Fractional differential equations, 1st edn. Elsevier Science & Technology Books, Amsterdam 4. Podlubny I (1999) Fractional-order systems and (PID mu)-D-lambda-controllers. IEEE Trans Automatic Control 44(1):208–214 5. Zhao C, Xue D, Chen Y-Q (2005) A fractional order PID tuning algorithm for a class of fractional order plants.

Astr¨om K, H¨agglund T (1995) PID controllers: Theory, design, and tuning. Instrument Society of America, North Carolina 2. Cao J, Cao BG (2006) Design of fractional order controller based on particle swarm optimisation. Int J Contr Autom Syst 4:775–781 4 Parameter Tuning of a Fractional-Order PI Controller Using the ITAE Criteria 57 3. Dorcak L, Petras I, Kostial I, Terpak J (2001) State-space controller desing for the fractionalorder regulated system. International carpathian control conference, Krynica, Poland 4.

One can argue that the neural network could be realized as a single hidden layer one, or with two hidden layers with less number of neurons in each. In obtaining the neural model, whose results are discussed, many trials have been performed, and it is seen that the approximation performance could be increased if there are more neurons in the hidden layers. In a similar fashion, a better map could be constructed if earlier values of the incoming error signal are taken into consideration. This enlarges the network size and makes it more intense computationally to train the model.

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