Jagdish Chandra Patra

Identification of nonlinear dynamic systems using functional link artificial neural networks

We have presented an alternate ANN structure called functional link ANN (FLANN) for nonlinear dynamic system identification using the popular backpropagation algorithm. In contrast to a feedforward ANN structure, i.e., a multilayer perceptron (MLP), the FLANN is basically a single layer structure in which nonlinearity is introduced by enhancing the input pattern with nonlinear functional expansion. With proper choice of functional expansion in a FLANN, this network performs as good as and in some cases even better than the MLP structure for the problem of nonlinear system identification.

Nonlinear channel equalization for QAM signal constellation using artificial neural networks

Application of artificial neural networks (ANNs) to adaptive channel equalization in a digital communication system with 4-QAM signal constellation is reported in this paper. A novel computationally efficient single layer functional link ANN (FLANN) is proposed for this purpose. This network has a simple structure in which the nonlinearity is introduced by functional expansion of the input pattern by trigonometric polynomials. Because of input pattern enhancement, the FLANN is capable of forming arbitrarily nonlinear decision boundaries and can perform complex pattern classification tasks. Considering channel equalization as a nonlinear classification problem, the FLANN has been utilized for nonlinear channel equalization. The performance of the FLANN is compared with two other ANN structures [a multilayer perceptron (MLP) and a polynomial perceptron network (PPN)] along with a conventional linear LMS-based equalizer for different linear and nonlinear channel models. The effect of eigenvalue ratio (EVR) of input correlation matrix on the equalizer performance has been studied. The comparison of computational complexity involved for the three ANN structures is also provided.

An intelligent pressure sensor using neural networks

In this paper, we propose a scheme of an intelligent capacitive pressure sensor (CPS) using an artificial neural network (ANN). A switched-capacitor circuit (SCC) converts the change in capacitance of the pressure-sensor into an equivalent voltage. The effect of change in environmental conditions on the CPS and subsequently upon the output of the SCC is nonlinear in nature. Especially, change in ambient temperature causes response characteristics of the CPS to become highly nonlinear, and complex signal processing may be required to obtain correct readout. The proposed ANN-based scheme incorporates intelligence into the sensor. It is revealed from the simulation studies that this CPS model can provide correct pressure readout within /spl plusmn/1% error (full scale) over a range of temperature variations from -20/spl deg/C to 70/spl deg/C. Two ANN schemes, direct modeling and inverse modeling of a CPS, are reported. The former modeling technique enables an estimate of the nonlinear sensor characteristics, whereas the latter technique estimates the applied pressure which is used for direct digital readout. When there is a change in ambient temperature, the ANN automatically compensates for this change based on the distributive information stored in its weights.

Modeling of an intelligent pressure sensor using functional link artificial neural networks

A capacitor pressure sensor (CPS) is modeled for accurate readout of applied pressure using a novel artificial neural network (ANN). The proposed functional link ANN (FLANN) is a computationally efficient nonlinear network and is capable of complex nonlinear mapping between its input and output pattern space. The nonlinearity is introduced into the FLANN by passing the input pattern through a functional expansion unit. Three different polynomials such as, Chebyschev, Legendre and power series have been employed in the FLANN. The FLANN offers computational advantage over a multilayer perceptron (MLP) for similar performance in modeling of the CPS. The prime aim of the present paper is to develop an intelligent model of the CPS involving less computational complexity, so that its implementation can be economical and robust. It is shown that, over a wide temperature variation ranging from -50 to 150 degrees C, the maximum error of estimation of pressure remains within +/- 3%. With the help of computer simulation, the performance of the three types of FLANN models has been compared to that of an MLP based model.

An ANN-based smart capacitive pressure sensor in dynamic environment

Abstract A multilayer artificial neural network (ANN) is proposed for modeling of a capacitive pressure sensor (CPS). When the ambient temperature changes over a wide range, the nonlinear response characteristics of a CPS change significantly. In many practical conditions, the effect of temperature on the change in the CPS characteristics may be nonlinear. The proposed ANN model can provide correct readout of the applied pressure under such conditions. A novel scheme for estimation of the ambient temperature from the sensor characteristics itself is proposed. A second ANN is utilized to estimate the ambient temperature from the knowledge of the offset capacitance, i.e., the zero-pressure capacitance. A microcontroller unit (MCU)-based implementation scheme for this model is also considered. Simulation results show that this model can estimate the pressure with a maximum error of ±2% over a wide variation of temperature from −50°C to 150°C.

Modeling and development of an ANN-based smart pressure sensor in a dynamic environment

Abstract In many engineering applications, a capacitive pressure sensor (CPS) is placed in a dynamic environment in which the temperature variation is quite large. Since the response characteristics of a CPS are highly nonlinear and temperature dependent, in such situations, complex signal processing techniques are needed to obtain correct readout of the applied pressure. We have proposed an artificial neural network (ANN)-based smart capacitive pressure sensor, whose response characteristics can be estimated within an accuracy of ±1% error over a wide variation of temperature starting from −50°C to 150°C. This modeling scheme automatically takes care of all the nonidealities, such as, nonlinearity, offset, gain and temperature dependence, of the sensor. A novel idea of automatic collection of temperature information and its feeding into the ANN model is also proposed. In the practical implementation of this scheme, the hardware complexity poses a serious impairment. Since the tanh() functions are needed for implementation in the ANN-based model, to reduce the hardware requirement, we provide a simple scheme for computation of tanh(). Sensitivity analysis of the model with respect to the finite word-length constraint on the final stored weight values, and number of terms used in the implementation of tanh() function, have been carried out. A microcontroller-based implementation scheme for the ANN-based model is also suggested.

An artificial neural network-based smart capacitive pressure sensor

Abstract A smart capacitive pressure sensor (CPS) using a multi-layer artificial neural network is proposed in this paper. A switched capacitor circuit (SCC) converts change in capacitance of the CPS due to applied pressure into a proportional voltage. The nonlinear characteristics of the CPS make the SCC output nonlinear. Further, due to dependence of the CPS characteristics on ambient temperature, the SCC output becomes quite complex for obtaining correct digital output of the applied pressure, especially when the ambient temperature varies with time and/or place. To circumvent this difficulty, an ANN is employed to model the sensor. By training the ANN model suitably, the digital readout of the applied pressure can be obtained which is independent of ambient temperature. A new idea for collecting temperature information from the sensor characteristics themselves, and automatic feeding of this information into the ANN-based CPS model is proposed. From the simulation results it is verified that the ANN model can give correct readout of the applied pressure within ±1% error (FS) over a wide range of temperature variation starting from −20°C to 70°C. This modeling technique of the CPS provides greater flexibility and accuracy in a changing environment.

Auto-calibration and -compensation of a capacitive pressure sensor using multilayer perceptrons

Using multilayer perceptrons (MLPs), a smart model for a capacitive pressure sensor (CPS) is proposed. When the ambient temperature changes, the nonlinear response characteristics of a CPS may vary widely. Under such conditions, calibration of the sensor and compensation of the nonlinear sensor characteristics to obtain correct readout becomes a difficult task. The proposed MLP model can provide automatic nonlinear compensation and calibration of the CPS characteristics. A microcontroller unit (MCU)-based implementation scheme for this model is also considered. Simulation results show that this model can estimate the pressure with a maximum full-scale error of +/- 1% over a variation of temperature from -50 to 150 degrees C.

An intelligent pressure sensor with self-calibration capability using artificial neural networks

The nonlinear response characteristics of a capacitive pressure sensor (CPS) changes when the ambient temperature changes widely. In such conditions, the calibration becomes difficult, and to obtain an accurate pressure readout, appropriate compensation of the CPS characteristics is needed. We propose an intelligent CPS using artificial neural networks (ANNs) to provide self-calibration and compensation. The proposed ANN model can provide automatic nonlinear compensation and calibration of the CPS characteristics. A microcontroller unit (MCU) based implementation scheme for this model is also considered. Simulation results show that this model can estimate the pressure with a maximum full-scale error of /spl plusmn/1% over a variation of temperature from -50 to 150/spl deg/C.

Performance Evaluation of an Equalizer using BLMS Adaptive Algorithm under Constrained Word Length

The finite precision issue of an adaptive digital equalizer of a communication channel is dealt in this paper. The equalizer is assumed to employ a computationally efficient Block Least Mean Square (BLMS) Finite Impulse Response (FIR) Adaptive Filter (AF). The concept of adaptation failure of the equalizer under constrained word length is discussed and an analysis is carried out to account for this effect using a new approach based on probability density. This analysis has led to an important relation between the Probability of Adaptation Failure (PAF), the word length and the filter length of the equalizer. This relation shows that for a specified PAF, the word length requirement decreases with increase in the filter length. This theoretical finding has also been verified by computer simulation. Exhaustive finite and infinite precision simulations reveal that below a word length of 6 bit (excluding the sign bit) the performance of the equalizer degrades drastically and at 9 bits its performance becomes i...