Journal of Engineering Science and Technology Review 9 (4) (2016) 74 - 76 Research Article A Novel Modeling Technique for Operational Amplifier Using RBF-ELM B.Shivalal Patro 1, * and Sushanta K. Mandal 2 1 School of Electronics Engineering, KIIT University, India 2 Department of Electronics and Telecommunication, CUTM University, India Received 22 June 2016; Accepted 9 September 2016 ___________________________________________________________________________________________ Abstract This paper formulates a new modeling technique for the analog circuits like OPAMP (Operational Amplifier). One of the fastest learning techniques, extreme learning machine along with best suitable kernel Radial Basis Function has been implemented for modeling. The simulated result shows that the training speed is very high and can handle a large set of data without compromising accuracy. Keywords: Analog Circuit, Gaussian Kernel, Machine learning, Feed-Forward Network, Mean Square Error. __________________________________________________________________________________________ 1. Introduction In recent years, researchers have shown greater interest in the modeling of circuits as the designing of circuits has come into a level of saturation. So, designers are taking the help of traditional design systems to integrate as well as modified forms to enhance the performance of the circuits. The modeling of analog, digital and mixed signal circuits has been carried out since several decades ago and presently the designers are depending on these modeling techniques to formulate some new designs [1, 2]. These formulations will help the designers in scaling the designs to its lower level by compromising the specifications into very less margin and hence can improve the efficiency of the circuit into a greater extent. The modeling of the circuits can be done on various aspects like behavioral form, analytical form, symbolic form, etc., without going into the details how each component is affecting the behavior of the circuit. That means the designer need not to be an expert in circuit design, he can analyze the relation between the output variations in accordance with the input. So, on the basis of mapping of the analog circuits of various models, many modeling techniques have been formulated [3]. To them various machine-learning algorithms are available which are able to map these circuits with reliable models which work as similar to the circuits [4, 5]. Machine learning is an area of artificial intelligence where various algorithms are formulated to develop various systems which can be able to perform classification, clustering, regression and many other activities. The prerequisites for these learning algorithms are the data which will help the system to develop a model which can be able to make predictions. So, in the conceptual modeling of analog circuits, RBF-ELM can play one of the best modeling techniques. 2. Proposed Technique Huang et al. [6] in 2004 proposed Extreme Learning Machine to provide a speedier approach to the neural networks for analyzing big data. In recent years, ELM (Extreme Learning Machine) has become one of the most preferable alternatives in machine learning algorithms. These machines are working on the principle SLFNN (Single Layer Feedforward Neural Network). SLFNN is an extension of neural network, which can be used to estimate a function from a given set of inputs and their corresponding outputs. It relates the inputs by choosing random weights with the outputs within an error β results to a trained network. For learning and tuning the input parameters slow gradient method is used. An activation function is provided to tune the network accordingly on the finite number of inputs and number of hidden neurons [7, 8]. ELM is mainly used for the problems where classification, clustering, regression are the main objectives of the systems. As they contain a single layer of hidden nodes or neurons where the weights connecting inputs to hidden neurons are randomly assigned. These assigned nodes are never trained or updated unlike other machine learning techniques, especially ANN (Artificial Neural Network) [9]. So, these hidden neurons, which are connected to the outputs, are learned in a single step. The fast training algorithm proceeded from non-iterative method is an aisle to the domain of large data set problems. It has been proven that when the sample of data approaches infinity the error between the estimated and actual result becomes zero [7]. So it becomes a very important factor regarding the variables which are used as inputs and their respective estimated outputs. So the designer who is training the ELM must preanalyze the data, whether there is any aberration or not. Adding RBF kernel to ELM will help the circuit designers achieve some vital design parameters to formulate new specification based circuits. If these things are not taken into consideration then the model may approach to over-fitting condition which will Jestr JOURNAL OF Engineering Science and Technology Review www.jestr.org ______________ * E-mail address: shivalalpatro@gmail.com ISSN: 1791-2377 © 2016 Eastern Macedonia and Thrace Institute of Technology. All rights reserved.