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It has some benefits over conventional optimization techniques such as only one tuning parameter, simple and solves the problems of local and premature convergence. This novel algorithm can solve complex optimization tasks efficiently. It is a metaheuristic algorithm which has fast convergence speed and large search space. The ISA algorithm is used for solving wide areas of optimization problems. It forms an algorithmic link between the search for global minima and thermodynamic behavior to solve discrete optimization issues.

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This algorithm has an analogy with the process in that a crystalline solid is heated and then cooled very slowly till it reaches the most regular possible crystal lattice arrangement (physical annealing of solids), and thus is free of crystal defects. Discrete and partly continuous optimization problems can be usually dealt with by SA. This technique is popular due to ease of execution, use of hill-climbing moves, and convergence properties. SA is a meta-heuristic algorithm that can escape from local optima.

MATLAB FILTER DESIGNER USE FILTER CODE

Various attempts have been taken to apply this approach to multiple problems in areas of VLSI design, code generation, and pattern recognition with significant achievement. Over the last few years, the SA technique has been attained great attention to obtain good solutions for challenging optimization problems. Section 8 discusses results and finally, the main facts are summarized in Sect. Section 7 emphasizes the analog realization of the proposed filter. Section 6 focuses on the comparison of proposed filters with existing counterparts. Section 5 deals with the performance parameters obtained using various techniques. Section 4 presents the stability analysis in W-plane. Section 3 highlights the use of SA, ISA and NLS optimization methods to obtain the filter coefficients. 2 describes the optimization techniques used in the proposed filter. This paper is organized as follows: Sect. It has the benefits of the differential difference amplifier and second generation current conveyor (CC-II). DVCC is an advanced and most effective block for realizing analog circuits. The best technique out of these three is chosen and then the proposed filter is realized using DVCC based circuit. In the proposed work, (2 + О±) order low pass Bessel filter is approximated using SA, ISA, and NLS optimization techniques. Here, higher order Bessel filter is designed using optimization techniques as it is not attempted previously. However, there is a need to design a higher order fractional filter. Thus, fractional order Butterworth, Chebyshev, Inverse Chebyshev, and Bessel filters have been designed using optimization techniques in the literature. In addition to these, the comparison of different optimization techniques for designing fractional filters (Butterworth, Chebyshev and Bessel) has also been done. realized fractional order Butterworth, Chebyshev, and Inverse Chebyshev filters using optimization techniques. Nowadays, the performance of fractional order filters is being improved by using optimization techniques. Further, the active and passive realization of fractional Butterworth filters has been done by Ali et al.

Initially, fractional order filters have been designed for first and second order systems. Where О“ (.) is the gamma function, m is an integer and О± is fractional order.

The MATLAB and SPICE results are shown in good agreement.

Monte Carlo and noise analyses are also performed for the proposed filter. The proposed filter is implemented for the cut-off frequency of 10В kHz using a wideband fractional capacitor. The circuit realization of 2.5 order low pass Bessel filter is done using two DVCCs (differential voltage current conveyors), one generalized impedance converter (GIC) based inductor, and one fractional capacitor. The simulated responses of the best optimized proposed filter are attained using the FOMCON toolbox of MATLAB and SPICE. The stability analysis of the proposed filter has also been done in W-plane. The best optimization technique based on the error in gain, cut-off frequency, roll-off, passband, stopband, and phase is chosen for designing the proposed filter. The coefficients of the proposed filter are obtained by minimizing the error between transfer functions of (2 + О±) order low pass filter and third-order Bessel approximation using simulated annealing (SA), interior search algorithm (ISA), and nonlinear least square (NLS) optimization techniques. This paper proposes the design and analysis of (2 + О±) order low pass Bessel filter using different optimization techniques.

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