Antonio Luchetta

On the application of symbolic techniques to the multiple fault location in low testability analog circuits

A new approach for the multiple fault location in linear analog circuits is proposed. It presents the characteristic of using classical numerical procedures together with symbolic analysis techniques, which is particularly useful in the parametric fault diagnosis field. The proposed approach is based on the k-fault hypothesis and is provided with efficient algorithms for fault location also in the case of low testability circuits. The developed algorithms have been used for realizing a software package prototype which implements a fully automated system for the fault location in linear analog circuits of moderate size.

A Method for the Automatic Selection of Test Frequencies in Analog Fault Diagnosis

A new procedure for the selection of test frequencies in the parametric fault diagnosis of analog circuits is presented. It is based on the evaluation of algebraic indices, as the condition number and the norm of the inverse, of a sensitivity matrix of the circuit under test. This matrix is obtained starting from the testability analysis of the circuit. A test index (T.I.) that permits the selection of the set of frequencies that better leads to locating parametric faults in analog circuits is defined. By exploiting symbolic analysis techniques, a program that implements the proposed procedure has been developed. It yields the requested set of frequencies by means of an optimization procedure based on a genetic algorithm that minimizes the T.I. Examples of the application of the proposed procedure are also included.

SAPWIN-a symbolic simulator as a support in electrical engineering education

Gaining an insight into circuit properties in electrical engineering classes can be achieved by using computer based tools. A computer program which combines symbolic and numerical simulation capabilities is of great help, because such a program provides students with automatic analysis tools. This paper presents the program SAPWIN, which has been developed to perform an automatic symbolic and numerical analysis of linear circuits. The paper presents program features, their development lines and fundamental aspects. Also, the educational purposes which are contained in the use of the program itself are presented.

A new symbolic method for analog circuit testability evaluation

Testability is a very useful concept in the field of circuit testing and fault diagnosis and can be defined as a measure of the effectiveness of a selected test point set. A very efficient approach for automated testability evaluation of analog circuits is based on the use of symbolic techniques. Different algorithms relying on the symbolic approach have been presented in the past by the authors and in this work noteworthy improvements on these algorithms are proposed. The new theoretical approach and the description of the subsequent algorithm that optimizes the testability evaluation from a computational point of view are presented. As a result, in the computer implementation the roundoff errors are completely eliminated and the computing speed is increased. The program which implements this new algorithm is also presented.

A new symbolic program package for the interactive design of analog circuits

A new program package which constitutes an environment for the interactive exploration and improvement of analog circuit topologies is presented in this paper. The environment is provided with functionalities which permit the graphical schematic entry of the circuit, the symbolic analysis, the approximation of the symbolic results, the use of an external numerical simulator and the graphical postprocessing of both the symbolic and numerical simulation results. These functionalities allow us to immediately evaluate the influence of both topology and component value changes on the circuit behavior. The result is useful for educational/training purposes and for the interactive synthesis of new high-performance analog circuits.

A modified learning algorithm for the multilayer neural network with multi-valued neurons based on the complex QR decomposition

In this paper, a modified learning algorithm for the multilayer neural network with the multi-valued neurons (MLMVN) is presented. The MLMVN, which is a member of complex-valued neural networks family, has already demonstrated a number of important advantages over other techniques. A modified learning algorithm for this network is based on the introduction of an acceleration step, performing by means of the complex QR decomposition and on the new approach to calculation of the output neurons errors: they are calculated as the differences between the corresponding desired outputs and actual values of the weighted sums. These modifications significantly improve the existing derivative-free backpropagation learning algorithm for the MLMVN in terms of learning speed. A modified learning algorithm requires two orders of magnitude lower number of training epochs and less time for its convergence when compared with the existing learning algorithm. Good performance is confirmed not only by the much quicker convergence of the learning algorithm, but also by the compatible or even higher classification/prediction accuracy, which is obtained by testing over some benchmarks (Mackey–Glass and Jenkins–Box time series) and over some satellite spectral data examined in a comparison test.

Symbolic Techniques for the Selection of Test Frequencies in Analog Fault Diagnosis

In this work symbolic methods are used for implementing a procedure for the selection of test frequencies in multifrequency parametric fault diagnosis of analog linear circuits. The proposed approach is based on the evaluation of the condition number and the norm of a sensitivity matrix of the circuit under test. This matrix is determined by exploiting the testability and ambiguity group concepts. A Test Error Index (T.E.I.) is obtained which permits to select the set of frequencies which better leads to locate parametric faults in analog linear circuits. A program implementing the proposed procedure has been realized by using symbolic techniques. Examples of application are also included.

A new symbolic approach for testability measurement of analog networks

A new approach for testability measurement of analog networks is presented. It is based on the use of symbolic techniques, that allow us to realize very simple testability evaluation algorithms. The new method presents noteworthy advantages from a computational point of view with respect to previous symbolic techniques of testability measurement developed by the authors in the past. In fact it does not require the computation of the sensitivities of the network functions, but it is based only on the study of the network function symbolic coefficients. A new theorem has been proved and the subsequent new algorithm permits to completely eliminate the roundoff errors and increase the computing speed. A brief introduction to the program which implements this new algorithm is also presented.

A neural architecture for the parameter extraction of high frequency devices [MMICs]

A novel optimization technique for the parameter identification of microwave monolithic integrated circuits is presented. It is based on a hybrid neural network whose learning process convergence allows the validation of the circuit approximated lumped model. The main feature of such a learning process is that no external desired signal is required and the neural network can be considered of the unsupervised type. Furthermore, the neural network output represents the lumped circuit parameter estimation.

A symbolic approach to the fault location in analog circuits

The increased complexity of electronic circuits due to technological improvement has caused the need of always more sophisticated testing and fault diagnosis methodologies. However, while for digital systems these methodologies have now reached full automation, for analog and mixed signal systems there is a lack of efficient and simple methods in this field. The aim of this work is to present a completely new methodology for the parametric fault diagnosis of linear analog circuits. The new method, which is based on the k-fault diagnosis hypothesis and takes into account tolerances and measurement errors, has been fully automated. The automatic fault diagnosis system has been developed by exploiting symbolic techniques, which permit a significant reduction in the computational complexity.