Automating IIR filter design by genetic algorithm

A Canonic-Signed-Digit-(CSD-) coded genetic algorithm (GA) is proposed to find the optimal design of robustly stable infinite impulse response digital filter (IIR). Under the characteristics of the CSD structure, the circuit of the filter can be simplified and also the calculation speed can be raised to increase the hardware’s efficiency. However, the design of CSD has a big challenge: the CSD structure of the system parameters will be destroyed by an optimal design procedure. To solve this problem, in this research a CSD-coded GA is proposed so that the CSD structure can be maintained. Moreover, the robustly stable IIR filters design problem is included in this paper. The robustness of the IIR filters is achieved by ensuring that all poles of the filters are located inside a diskD(α,r)contained in the unit circle, in whichαis the center,ris the radius of the disk, and|α|+r<1. Consequently, in this paper, a new and more efficientD(α,r)-stability criterion will be derived and then embedded in GA for the design of robust IIR filters. It is worthwhile to note that to design an IIR filter simultaneously with CSD-structured parameters and robust stability is difficult and is not well explored so far. An example will be presented to show the efficiency of the proposed strategy for design of IIR filters.

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Multi-objective IIR Filter Design using Nondominated Sorting Genetic Algorithm-II

Indian Journal of Science and Technology ◽

10.17485/ijst/2015/v8i1/102764 ◽

2016 ◽

Vol 9 (47) ◽

Author(s):

Rajesh Chandrakant Sanghvi ◽

Himanshu B. Soni

Keyword(s):

Genetic Algorithm ◽

Filter Design ◽

Multi Objective ◽

Iir Filter ◽

Nondominated Sorting

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Control loop oscillation detection and quantification using PRONY method of IIR filter design and deep neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189778 ◽

2021 ◽

pp. 1-14

Author(s):

Sachin Sharma ◽

Vineet Kumar ◽

K.P.S. Rana

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Filter Design ◽

Support Vector ◽

Control Loop ◽

External Interference ◽

Iir Filter ◽

Prony Method ◽

Oscillation Detection ◽

Detection And Quantification

Generally, the process industry is affected by unwanted fluctuations in control loops arising due to external interference, components with inherent nonlinearities or aggressively tuned controllers. These oscillations lead to production of substandard products and thus affect the overall profitability of a plant. Hence, timely detection of oscillations is desired for ensuring safety and profitability of the plant. In order to achieve this, a control loop oscillation detection and quantification algorithm using Prony method of infinite impulse response (IIR) filter design and deep neural network (DNN) has been presented in this work. Denominator polynomial coefficients of the obtained IIR filter using Prony method were used as the feature vector for DNN. Further, DNN is used to confirm the existence of oscillations in the process control loop data. Furthermore, amplitude and frequency of oscillations are also estimated with the help of cross-correlation values, computed between the original signal and estimated error signal. Experimental results confirm that the presented algorithm is capable of detecting the presence of single or multiple oscillations in the control loop data. The proposed algorithm is also able to estimate the frequency and amplitude of detected oscillations with high accuracy. The Proposed method is also compared with support vector machine (SVM) and empirical mode decomposition (EMD) based approach and it is found that proposed method is faster and more accurate than the later.

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