Artificial neural networks  give the advantage of execution improvement through learning using parallel and distributed processing. These networks are carried out using huge connections among processing units with changeable brawn, and they are enticing, of applications in System Identification and control . It uses a neural network called CMAC , which stands for Cerebellar Model Arithmetic Computer. The CMAC net appertains for system identification. This network is second to inseminate multi-layer networks in real-time applications. CMAC has properties of conjecture, rapid algorithmic estimation based on LMS  training, practical representation, and output incrustation. CMAC network has hundreds of thousands of convertible weights that can be direct to imprecise nonlinearities which are not written out or foggiest idea. CMAC can learn nonlinear association from a very comprehensive category of functions. CMAC is an associative memory  that has an integral conjecture. The architecture of CMAC is alike to that of the cerebellum (a part of the brain).
System Identification complications can be observed as delineate betwixt the inputs and outputs of the identification block. The identification block lay hold of the current input and output variables of the process as its inputs and gives the estimates of the system parameters. The ply of CMAC network in understanding the required delineate. Its precedence in system identification of both linear and nonlinear systems mentioned .
The simulated network is carried out to approximate the damping coefficient of a nonlinear pendulum subjected to changeable driving force and viscous friction. The network is imitated for dissimilar learning rates and different number of training data points . It is appearing that CMAC network can appear for dynamic systems too. The network meets smoothly if the learning rate is average. The network’s staging is supercilious when the training data points are analogously distributed over the whole input scale instead of random points.
Kalinga Plus is an initiative by Kalinga University, Raipur. The main objective of this to disseminate knowledge and guide students & working professionals.
This platform will guide pre – post university level students.
Pre University Level – IX –XII grade students when they decide streams and choose their career
Post University level – when A student joins corporate & needs to handle the workplace challenges effectively.
We are hopeful that you will find lot of knowledgeable & interesting information here.