Lyapunov Theory-Based Multilayered Neural Network

This brief presents a Lyapunov theory-based weight adaptation scheme for a multilayered neural network (MLNN) mainly used to classify a multiple-input-multiple-output (MIMO) problem. Initially, the MLNN system is linearized using Taylor series expansion. Then, the weight adaptation scheme is designed based on the Lyapunov stability theory to iteratively update the weight. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Hence, the Lyapunov theory-based MLNN acts as a MIMO classifier for face recognition. Analysis and discussion on Lyapunov properties of the proposed classifier are included. The performance of the proposed technique is tested on the Olivetti Research Laboratory database for face classification, and some comparisons with existing conventional techniques are given. Simulation results have revealed that our proposed system achieved better performance.     

Passive filter design using genetic algorithms

The objective of this paper is to propose a new approach for designing a passive LC filter of the full-bridge rectifier by using genetic algorithms (GAs). The performance of the cost-effective passive LC filter for a constant load depends on the appropriate inductor and capacitor selection. Several design methods are reviewed and a novel design methodology using GAs is proposed in this paper. By using the proposed GA program, designer can quickly find appropriate parameter values to meet the desired circuit performance. Experimental results show that an appropriate combination of the inductor and capacitor selected by the proposed GA program can meet the desired power quality requirement. Different cases of design examples are shown in this paper to verify the performance of the proposed design methodology.     

Comparative analysis of the Lyapunov function for different strategies of analogue circuits design

Generalized methodology has been developed for analogue circuits design based on applying the optimal control theory. The problem of time optimal system design was defined as a classical functional optimization problem of the optimal design theory. In this case the process of analogue circuits design is mathematically defined as a dynamic control system. In this context the minimization problem of the processor time of designing can be formulated as a minimization problem of the response time of dynamic system. In order to analyze the properties of such a system, it is proposed to use the concept of the Lyapunov dynamic system function. Using this function and its time derivative a special function has been built that allows us to predict the total processor time of circuit design by using the characteristics of the initial period of designing. Numerical results indicate the possibility of predicting the processor time of different design strategies in terms of the special function behavior.     

Development of Lyapunov-Based Genetic Algorithm Control for Linear Piezoelectric Ceramic Motor Drive

In general, the role of genetic algorithm (GA) is operated offline as a minor compensator or tuner in the control engineering because the systematic design and the latent stability problem of a GA-based control scheme are required to be solved. This paper originally designs a Lyapunov-based GA control (LGAC) scheme, and it applies for a practical control engineering example of the online motion control of a linear piezoelectric ceramic motor driven by a hybrid resonant inverter. In this control scheme, a GA control system via backstepping design technique is utilized to be the major controller, and adaptation laws derived from Lyapunov stability analyses are manipulated to adjust appropriate evolutionary steps. As a result, the system stability can be guaranteed directly without strict constraint conditions and detailed system knowledge. The effectiveness of the proposed drive and control system is verified by experimental results in the presence of uncertainties. From the measured results, the LGAC system performs superior high-precision motion control under wide operation range than conventional backstepping control system.     

An FIR Equalizer Design for Nonlinear Channels Using Hybrid GA and LMI

In this paper, a finite impulse response (FIR) equalizer for nonlinear discrete-time channels is designed by employing a hybrid genetic algorithm (GA) and linear matrix inequality (LMI) approach from an H_∞ perspective. The GA technique is utilized to linearize the nonlinear channel model, and the approximate error can be viewed as a state uncertainty. Then, the design of the FIR equalizer is transformed into LMIs, and the coefficients of the FIR equalizer can be obtained by solving an LMI optimization problem. Finally, numerical examples are included to illustrate the effectiveness of the proposed methodology.     

Automatic design of frequency sampling filters by hybrid geneticalgorithm techniques

A new method of designing recursive and nonrecursive frequency sampling filters is presented. We investigate the use of a hybrid real-coded genetic algorithm (GA) for optimising transition sample values to give the maximum stopband attenuation. A modification allows the coefficient wordlength to be optimized concurrently, thereby reducing the overall number of design steps and simplifying the design process. The technique is able to consistently optimize filters with up to six transition samples. Designing digital filters is a complex process involving optimization at several discrete design steps. The techniques presented could form the basis for integrating several of the optimizations. Investigations into increasing this integration by using a binary-coded GA to optimize nonlinear phase, quantized coefficient FIR filters are introduced, with an analysis of the difficulty of the problem from a GA perspective     

Topology design and bandwidth allocation of embedded ATM networksusing genetic algorithm

A genetic algorithm (GA) is proposed for the design of an asynchronous transfer mode (ATM) network embedded in a digital cross connect network. This highly constrained optimization problem is shown to be better solved using GA. Improved results, both in network performance and computation speed, are obtained when comparing with the existing heuristic approach. A similar methodology can be applied for the design of other communication networks     

一种新的调制型混合滤波器组设计算法

针对经典的准确重构混合滤波器组设计问题,提出一种调制型混合滤波器组(MHFB)的设计算法,推导了分析滤波器组系数矩阵的行列式与原型滤波器系数的解析关系,给出了具有普适性的综合滤波器组解的一般形式,并讨论了因果、稳定系统的设计方法。针对多通道HFB设计复杂的问题,提出一种FIR形式综合滤波器组的优化设计算法,适用于多通道HFB的设计。仿真结果验证了算法的有效性。     

Optimal design of digital IIR filters by using hybrid taguchi genetic algorithm

A hybrid Taguchi genetic algorithm (HTGA) is applied in this paper to solve the problem of designing optimal digital infinite-impulse response (IIR) filters. The HTGA approach is a method of combining the traditional GA (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimum offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Based on minimizing the L/sub p/-norm approximation error and minimizing the ripple magnitudes of both passband and stopband, a multicriterion combination is employed as the design criterion to obtain the optimal IIR filter that can fit different performance requirements. The proposed HTGA approach is effectively applied to solve the multiparameter and multicriterion optimization problems of designing the digital low-pass (LP), high-pass (HP), bandpass (BP), and bandstop (BS) filters. In these studied problems, there are many parameters and numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed GA-based approaches. The computational experiments show that the proposed HTGA approach can obtain better digital IIR filters than the existing GA-based method reported recently in the literature.     

实数遗传算法的改进及性能研究

提出一种粒子群优化方法(PSO)与实数编码遗传算法(GA)相结合的混合改进遗传算法(HIGAPSO).该方法采用混沌序列产生初始种群、非线性排序选择、多个交叉后代竞争择优和变异尺度自适应变化等改进遗传操作;并通过精英个体保留、粒子群优化及改进遗传算法(IGA)三种策略共同作用产生种群新个体,来克服常规算法中收敛速度慢、早熟及局部收敛等缺陷.通过四个高维典型函数测试结果表明该方法不但显著提高了算法的全局搜索能力,加快了收敛速度;而且也改善了求解的质量及其优化结果的可靠性,是求解优化问题的一种有潜力的算法.     

Hybrid Genetic Algorithm for the Design of Modified Frequency-Response Masking Filters in a Discrete Space

This paper presents the design of high-speed, arbitrary bandwidth sharp finite impulse response filters with signed powers-of-two coefficients based on a modified frequency-response masking (FRM) structure. A novel hybrid genetic algorithm (HGA) is proposed to jointly optimize all subfilters in a discrete space. The proposed HGA introduces the simulated annealing technique into the genetic algorithm (GA) optimization process and effectively prevents the GA from prematurely converging. It is shown, by means of examples, that FRM filters designed by the HGA achieve a significant reduction in the number of bits.     

Support Vector Driven Genetic Algorithm for the Design of Circular Polarized Microstrip Antenna

In this paper, a hybrid soft computing method for designing specific microstrip antenna is presented. Evolutionary algorithm such as genetic algorithm (GA) is one of the promising ways of finding global optimum solution from a multivariate nonlinear feature space. Being a stochastic iterative algorithm, it requires much computation power when the function to be optimized is complex and time consuming. Various meta-modelling techniques such as neural network, response surface methods, kriging, etc. can be used to model the process under optimization in order to reduce the computational expenses. In this paper, we investigate one such technique – support vector regression (SVR) – to model the complex analytical process. The model, thus obtained, is used for optimization using genetic algorithms. This approach is demonstrated for the design of circular polarized microstrip antenna at 2.6 GHz band. The results of SVR model are compared with other meta-models generated with neural network and response surface methodology.     

Shape inversion of metallic cavities using hybrid genetic algorithm combined with tabu list

An approach combining the hybrid genetic algorithm (GA) with the tabu list concept is proposed to increase the search efficiency of the hybrid GA. The algorithm is applied to reconstruct the shape of a metallic cavity based on the Ipswich measurement data. Inversion results show good agreement with the actual shape and significant improvement in convergence rate over both simple GA and hybrid GA.     

Application of genetic algorithm in computing the tradeoffs between power consumption versus delay in digital integrated circuit design

This paper presents a novel methodology to obtain the entire power consumption versus delay tradeoff curve for the critical paths of a combinational logic circuit in a very efficient way using the genetic algorithm (GA). In order to evaluate the proposed algorithm the most representative set of two-level and multi-level networks from the MCNC91 benchmark suite were processed. The required computational effort, measured in terms of CPU time, is several times better for the proposed GA optimization technique than liner programming (LP) technique. On the other hand, the optimal design points obtained by the GA and LP techniques are very close to each other to within 0.3%.     

具有较大鲁棒度的歼击机容错控制

针对复杂非线性系统被动容错控制问题,提出将AREs与小生境遗传算法相结合的方法。在T-S建模的基础上,这种方法用于搜寻最优控制器。在小生境遗传算法中提出了交叉变异概率的自适应调整律,克服遗传算法早熟的缺点。为了消除T-S建模误差,应用在线可调且具有补偿项的RBFN来消除建模误差。歼击机在多种故障下的仿真表明,此方法能够实现很好的容错控制效果。     

RF Class-E Power Amplifier Design Based on a Load Line-Equivalent Capacitance Method

In this research work, an alternative methodology to design radio frequency (RF) class-E amplifier is proposed and verified. The conventional design procedure starts with a switch idealization of the device and extraction of the output capacitance based on S parameter measurements. Since these ideal conditions lead to non-optimal values at microwave frequencies, an optimization procedure on these values, using a non-linear model of the device, has to be applied in order to obtain higher performance. Unlike this legacy procedure, the proposed method starts the design of the RF class E power amplifier with the non-linear model directly to determine a more representative value of the equivalent capacitance, and thereby considering all the non-ideal and non-linear features of the device.     

A new method for designing multiplierless two-channel filterbank using shifted-Chebyshev polynomials

This paper presents an efficient design method for a digital multiplierless two-channel filterbank using the shifted-Chebyshev polynomials and common sub-expression elimination (CSE) algorithm for reducing hardware requirements such as adders and multipliers. For designing a two-channel filterbank, the design problem is constructed as minimization of integral mean square error between the desired and designed response of a prototype filter in the passband and stopband. For controlling the performance in passband and stopband, two parameters (K_P, and K_S) are used, whose optimum values are determined by swam optimization techniques such as differential evolution algorithm, artificial bee colony optimization, particle swarm optimizations, cuckoo search algorithm and hybrid method using a fitness function, constructed by perfect reconstruction condition of a filterbank. The number of polynomials used for approximation depends upon the order of a prototype filter. A new hybrid CSE is proposed for further reduction of hardware requirement. A comparative study of various CSE techniques such as horizontal, vertical and proposed hybrid CSE is also made. Numerical examples illustrate the effectiveness of the proposed algorithm in the reduction of adders with comparisons accomplished using existing methods. It has been found that almost 43% adder gain can be achieved when a filter is designed with N = 32 and wordlength (WL) as 12 using proposed methodology.     

Error-rate analysis of polar code designs using genetic algorithm for additive white Gaussian noise channel

A novel scheme for designing polar codes with specific decoding schemes in the additive white Gaussian noise channel is presented in this paper. The code construction strategy is built on the genetic algorithm (GA) where successive evolution of populations (or group of information sets) leads toward fittest candidate to attain finest error-rates. In this work, it is shown that better error-rates can be attained by both successive cancelation list decoding using GA with no cyclic redundancy check (CRC) and belief propagation decoding using GA with no CRC and no list, compared to existing polar decoding schemes. Our proposed polar code design scheme using GA has the ability to attain a target block error rates with the least possible SNR and using no additional CRC by exploiting least belief propagation iterations or lesser successive cancelation list list size with no self-alterations in the decoding algorithm.     

Chaos synchronization using higher-order adaptive PID controller

This paper is devoted to designing higher-order adaptive PID controllers as a new generation of PID controllers for chaos synchronization, in which second order integration and second-order derivative terms to the PID controller (PII²DD²) are employed. The five PII²DD² control gains are updated online with a stable adaptation law driven by Lyapunov’s stability theory. This is the unique advantage of the proposed approach. Furthermore, it is equipped with a novel robust control term to improve controller robustness against system uncertainties and unknown disturbances. An important feature of the proposed approach is that it is a model-free controller. In addition, to determine the control design parameters and avoid trial and error, the Teaching–learning-based optimization algorithm (TLBO) is employed to regulate these parameters and enhance the performance of the proposed controller. Based on the Lyapunov stability theory, it is proven that the proposed control scheme can guarantee the synchronization and the stability of closed-loop control system. The case study is the Duffing–Holmes oscillator. Comparative simulation results are presented which confirm the superiority of the proposed approach.     

空空导弹三维自适应模糊滑模制导律设计

提出了一种基于自适应模糊滑模控制的空空导弹三维制导律,解决了一般滑模控制中存在的抖振问题.该制导律在非线性系统的精确模型未知的情况下,通过模糊系统对非线性模型进行逼近,并根据Lyapunov方法设计了参数自适应律.仿真结果表明,该制导律与比例制导相比有较大的性能改善.