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A Nonlinear MESFET Model for Intermodulation Analysis Using a Generalized Radial Basis Function Network
"... In this paper we use a Generalized Radial Basis Function (GRBF) network to model the intermodulation properties of microwave GaAs MESFET transistors under dynamic operation. The proposed model receives as input the bias voltages of the transistor and provides as output the derivatives of the draint ..."
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In this paper we use a Generalized Radial Basis Function (GRBF) network to model the intermodulation properties of microwave GaAs MESFET transistors under dynamic operation. The proposed model receives as input the bias voltages of the transistor and provides as output the derivatives of the draintosource current, which are responsible for the intermodulation properties. The GRBF network is a generalization of the RBF network, which allows different variances for each dimension of the input space. This modification allows to take advantage of the soft nonlinear dependence of the output derivatives with the draintosource bias voltage. The learning algorithm chooses the GRBF centers one by one in order to minimize the output error. After selecting each new center from the training set, the centers and variances of the global network are optimized by applying gradient descent techniques. Finally, the amplitudes are obtained by solving a least squares problem. The effectiveness of the ...
High Order Volterra Series Analysis Using Parallel Computing
"... INTRODUCTION The Volterra series technique has been used extensively in various applications in the area of nonlinear circuit analysis and optimization (see e.g. references [1][28]). Examples are in the (i) analysis of intermodulation in small signal amplifiers [6][12], (ii) determination of os ..."
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INTRODUCTION The Volterra series technique has been used extensively in various applications in the area of nonlinear circuit analysis and optimization (see e.g. references [1][28]). Examples are in the (i) analysis of intermodulation in small signal amplifiers [6][12], (ii) determination of oscillation frequency and amplitude in near sinusoidal oscillators [3][5], (iii) analysis of mixers with moderate local oscillator levels [13, 14], analysis of communication systems [14][18], and (v) analysis of noise in nonlinear networks [24][28]. The use of the Volterra series technique basically involves two steps: (i) first, from specified input signal frequencies to determine all relevant Volterra transfer functions of the network, and (ii) next, to determine the output response from the nonlinear network based on specified amplitudes of the input signals. One limitation in the use of Volterra series is that the determination of Volterra transfer functions is usually limi
A Modular Neural Network for Global Modeling of Microwave Transistors
, 2000
"... In this paper we present a modular neural network structure for global modeling of microwave transistors (MESFET/HEMT). The model is able to accurately represent both, the smallsignal and the largesignal behavior of the device. This is achieved by means of an original neural architecture, which is ..."
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In this paper we present a modular neural network structure for global modeling of microwave transistors (MESFET/HEMT). The model is able to accurately represent both, the smallsignal and the largesignal behavior of the device. This is achieved by means of an original neural architecture, which is composed of two main modules. The first module captures the nonlinear dynamic I/V characteristic of the transistor, which governs the large signal behavior of the device. The second module estimates the derivatives at the operation (bias) point by means of a neural network and then it locally reconstructs the function by means of a third order Taylor series around that point. This second module is able to reproduce the smallsignal intermodulation behavior. These two modules are combined into a global model by means of a simple fuzzy controller. In this way the global model represents adequately the device behavior independently of the nature of the applied signals. I. Introduction The d...
The Frequency Domain Behavioral Modeling and Simulation of Nonlinear Analog Circuits and Systems
, 1993
"... LUNSFORD II, PHILIP J. The Frequency Domain Behavioral Modeling and Simulation of Nonlinear Analog Circuits and Systems. (Under the direction of Michael B. Steer.) A new technique for the frequencydomain behavioral modeling and simulation of nonautonomous nonlinear analog subsystems is presented. ..."
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LUNSFORD II, PHILIP J. The Frequency Domain Behavioral Modeling and Simulation of Nonlinear Analog Circuits and Systems. (Under the direction of Michael B. Steer.) A new technique for the frequencydomain behavioral modeling and simulation of nonautonomous nonlinear analog subsystems is presented. This technique extracts values of the Volterra nonlinear transfer functions and stores these values in binary files. Using these files, the modeled substem can be simulated for an arbitrary periodic input expressed as a finite sum of sines and cosines. Furthermore, the extraction can be based on any circuit simulator that is capable of steady state simulation. Thus a large system can be divided into smaller subsystems, each of which is characterized by circuit level simulations or lab measurements. The total system can then be simulated using the subsystem characterization stored as tables in binary files.
A Coherent Small/Large Signal FET model Based on Neuronal Architectures 1
"... A modular neural network architecture for accurate small/large signal microwave MESFET/HEMT modeling is presented. This is achieved by means of an original neural architecture having two main modules. A network captures the nonlinear dynamic Pulsed I/V characteristic of the device, which is mainly r ..."
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A modular neural network architecture for accurate small/large signal microwave MESFET/HEMT modeling is presented. This is achieved by means of an original neural architecture having two main modules. A network captures the nonlinear dynamic Pulsed I/V characteristic of the device, which is mainly responsible of the large signal behavior, while the second network estimates the high order derivatives at the operation point, which are responsible of the IMD behaviour, by means of a neural network and then it locally reconstructs the current function by means of a third order Taylor series around that point. Finally, in order to have a maximum of coherence, the two networks are combined into a global model by means of a simple fuzzy controller. Computer simulations and experimental measurements validate this flexible modeling technique.