三元物系液-液平衡计算的Fsolve算法

本文基于MATLAB优化工具箱中的fsolve算法,提出了一种计算三元物系液-液平衡数据的新方法.应用该方法预测了水(1) 醋酸(2) 氯仿(3)物系在298 K,308 K和318 K的液-液平衡数据,并将298K的液-液平衡数据与文献数据进行了比较,它们非常一致.在本论文中,应用UNIFAC液-液平衡模型,采用H2O,CH3,COOH和CHCl3基闭相互作用参数计算该物系共轭相中各组分的活度系数,获得了多组结点数据及相的比率.各温度下计算结果显示:各三元液-液相平衡模型约束方程的剩余均接近于零向量,数量级在10-4以下.Other-Tobias关联得到线性相关系数分别足0.9863,0.9939和0.9976.总之,本算法的可靠性,稳定性适用于计算三元液-液相平衡,计算实例所得结果经过检验是正确的.     

Multi-objective Genetic Algorithms for grouping problems

Linear Linkage Encoding (LLE) is a convenient representational scheme for Genetic Algorithms (GAs). LLE can be used when a GA is applied to a grouping problem and this representation does not suffer from the redundancy problem that exists in classical encoding schemes. LLE has been mainly used in data clustering. One-point crossover has been utilized in these applications. In fact, the standard recombination operators are not suitable to be used with LLE. These operators can easily disturb the building blocks and cannot fully exploit the power of the representation. In this study, a new crossover operator is introduced for LLE. The operator which is named as group-crossover is tested on the data clustering problem and a very significant performance increase is obtained compared to classical one-point and uniform crossover operations. Graph coloring is the second domain where the proposed framework is tested. This is a challenging combinatorial optimization problem for search methods and no significant success has been obtained on the problem with pure GA. The experimental results denote that GAs powered with LLE can provide satisfactory outcomes in this domain, too.     

基于核函数的稳健线性嵌入方法

LLE算法是一种新的非监督学习方法,主要针对非线性降维问题。针对该算法存在的缺点,提出了一种基于核函数的稳健线性嵌入方法,该方法通过引进核函数来优化算法邻域点的求解;在特征空间中,修正权值矩阵W,进行降噪处理,经过推导,最终将实际的子空间计算归结为标准的特征值分解问题。采用最小近邻分类器估算识别率。在Yale人脸库以及AT&T人脸库的测试结果表明,在姿态、光照、表情、训练样本数目变化的情况下,改进的算法都具有较好的识别率。     

Optimizing the auto-brazing process quality of aluminum pipe and flange via a Taguchi-Neural-Genetic approach

This work describes an application of an integrated approach using the Taguchi method (TM), neural network (NN) and genetic algorithm (GA) for optimizing the lap joint quality of aluminum pipe and flange in automotive industry. The proposed approach (Taguchi-Neural-Genetic approach) consists of two phases. In first phase, the TM was adopted to collect training data samples for the NN. In second phase, a NN with a Levenberg-Marquardt back-propagation (LMBP) algorithm was adopted to develop the relationship between factors and the response. Then, a GA based on a well-trained NN model was applied to determine the optimal factor settings. Experimental results illustrated the Taguchi-Neural-Genetic approach.     

Aerodynamic shape optimization using efficient evolutionary algorithms and unstructured CFD solver

An efficient evolutionary algorithm is presented for shape optimization of transonic airfoils. Several techniques have been used to improve the efficiency and convergence rate of the optimization Genetic Algorithm (GA). A new airfoil shape parameterization method is used which is capable of producing more efficient shapes at viscous flow conditions. A Real-Coded Population Dispersion (PD) Genetic Algorithm is developed in order to increase the robustness and convergence rate of the Genetic Algorithm. A Multi-Layer Perceptron Neural Network (NN) is utilized to reduce the huge computational cost of the objective function evaluation. Further improvement in the performance of NN is obtained by using dynamic retraining and normal distribution of the training data to determine well trained parts of the design space to NN. Using the above techniques, the total computational time of optimization algorithm is reduced up to 60% compared with the conventional GA.     

A hybrid particle swarm–Nelder–Mead optimization method for crack detection in cantilever beams

The estimation of a crack location and depth in a cantilever beam is formulated as an optimization problem and the optimal location and depth are found by minimizing the cost function which is based on the difference of the first four measured and calculated natural frequencies. Calculated natural frequencies are obtained using a rotational spring model of the crack, and measured natural frequencies are obtained by using cracked beam frequency response and modal analysis. A hybrid particle swarm–Nelder–Mead (PS–NM) algorithm is used for estimating the crack location and depth. The hybrid PS–NM is made-up of a modified particle swarm optimization algorithm (PSO), aimed to identify the most promising areas, and a Nelder–Mead simplex algorithm (NM) for performing local search within these areas. The PS–NM results are compared with those obtained by the PSO, a hybrid genetic–Nelder–Mead algorithm (GA–NM) and a neural network (NN). The proposed PS–NM method outperforms other methods in terms of speed and accuracy. The average estimation errors for crack location and depth are (0.06%, 0%) for the PS–NM, however, (0.09%, 0%), (0.46%, 0.54%) and (0.39%, 1.66%) for the GA–NM, the PSO and the NN methods, respectively. To validate the proposed method and investigate the modeling and measurement errors some experimental results are also included. The average values of experimental location and depth estimation errors are (9.24%, 8.56%) for the PS–NM, but (9.64%, 9.50%), (10.89%, 10.89%), (11.53%, 11.64%) for the GA–NM, the PSO and the NN methods, respectively.     

Soft computing in medicine

Soft computing (SC) is not a new term; we have gotten used to reading and hearing about it daily. Nowadays, the term is used often in computer science and information technology. It is possible to define SC in different ways. Nonetheless, SC is a consortium of methodologies which works synergistically and provides, in one form or another, flexible information processing capability for handling real life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve tractability, robustness and low-cost solutions. SC includes fuzzy logic (FL), neural networks (NNs), and genetic algorithm (GA) methodologies. SC combines these methodologies as FL and NN (FL–NN), NN and GA (NN–GA) and FL and GA (FL–GA). Recent years have witnessed the phenomenal growth of bio-informatics and medical informatics by using computational techniques for interpretation and analysis of biological and medical data. Among the large number of computational techniques used, SC, which incorporates neural networks, evolutionary computation, and fuzzy systems, provides unmatched utility because of its demonstrated strength in handling imprecise information and providing novel solutions to hard problems.The aim of this paper is to introduce briefly the various SC methodologies and to present various applications in medicine between the years 2000 and 2008. The scope is to demonstrate the possibilities of applying SC to medicine-related problems. The recent published knowledge about use of SC in medicine is researched in MEDLINE. This study detects which methodology or methodologies of SC are used frequently together to solve the special problems of medicine. According to MEDLINE database searches, the rates of preference of SC methodologies in medicine were found as 68% of FL–NN, 27% of NN–GA and 5% of FL–GA. So far, FL–NN methodology was significantly used in medicine. The rates of using FL–NN in clinical science, diagnostic science and basic science were found as %83, %71 and %48, respectively. On the other hand NN–GA and FL–GA methodologies were mostly preferred by basic science of medicine.Another message emerging from this survey is that the number of papers which used NN–GA methodology has continuously risen until today. Also search results put the case clearly that FL–GA methodology has not applied well enough to medicine yet. Undeniable interest in studying SC methodologies in genetics, physiology, radiology, cardiology, and neurology disciplines proves that studying SC is very fruitful in these disciplines and it is expected that future researches in medicine will use SC more than it is used today to solve more complex problems.     

一种基于多物种协同进化的神经网络优化方法

论文在阐明了遗传算法和神经网络结合的必要性之后,分析了一般遗传算法在神经网络结构优化过程中存在的不足,并根据多物种之间相互竞争和相互适应的机理提出了一种基于多物种协同进化的优化方法。该方法既可以有效地避免神经网络结构寻优过程中解搜索空间过大以及进化规则复杂等问题,还可以起到对网络的结构和权值同时进化的作用。仿真实验表明该方法是可行并且有效的。     

A Neurogenetic approach for the resource-constrained project scheduling problem

A variety of metaheuristic approaches have emerged in recent years for solving the resource-constrained project scheduling problem (RCPSP), a well-known NP-hard problem in scheduling. In this paper, we propose a Neurogenetic approach which is a hybrid of genetic algorithms (GA) and neural-network (NN) approaches. In this hybrid approach the search process relies on GA iterations for global search and on NN iterations for local search. The GA and NN search iterations are interleaved in a manner that allows NN to pick the best solution thus far from the GA pool and perform an intensification search in the solution's local neighborhood. Similarly, good solutions obtained by NN search are included in the GA population for further search using the GA iterations. Although both GA and NN approaches, independently give good solutions, we found that the hybrid approach gives better solutions than either approach independently for the same number of shared iterations. We demonstrate the effectiveness of this approach empirically on the standard benchmark problems of size J30, J60, J90 and J120 from PSPLIB.     

基于遗传算法的神经网络性能优化

遗传算法是一种典型的进化算法。文中分析了遗传算法的特点和神经网络的特点,从而得出了把两种算法结合起来进行应用的思想。运用理论对比的方法,阐明了用遗传算法进行神经网络性能优化的原因,并得出结论,认为用遗传算法进行神经网络性能优化促使了神经网络更进一步的应用。阐述了遗传算法优化神经网络的两种主要方法,论述了遗传算法和神经网络的发展现状和将来的研究动向。     

Neuro evolutionary model for weld residual stress prediction

Welding is an efficient reliable metal joining process in which the coalescence of metals is achieved by fusion. Localized heating during welding, followed by rapid cooling, induce residual stresses in the weld and in the base metal. Determination of magnitude and distribution of welding residual stresses is essential and important. Data sets from finite element method (FEM) model are used to train the developed neural network model trained with genetic algorithm and particle swarm optimization (NN–GA–PSO model). The performance of the developed NN–GA–PSO model is compared neural network model trained with genetic algorithm (NN–GA) and neural network model trained with particle swarm optimization (NN–PSO) model. Among the developed models, performance of NN–GA–PSO model is superior in terms of computational speed and accuracy. Confirmatory experiments are performed using X-ray diffraction method to confirm the accuracy of the developed models. These developed models can be used to set the initial weld process parameters in shop floor welding environment.     

基于遗传算法的神经网络自适应控制器的研究

提出了一种基于遗传算法的神经网络自适应控制方法。该方法是针对ＢＰ算法训练神经网络控制系统时收敛速度慢、动态特性不够理想等不足，用改进的遗传算法来优化神经网络辨识器与控制器的参数，以提高控制系统的性能，仿真实验表明该控制器对于非线性、时变、滞后等对象都具有很好的控制精度、鲁棒性和动态特性。     

A neurocomputing model for real coded genetic algorithm with the minimal generation gap

This paper proposes using neural networks (NN) to implement a real coded genetic algorithm (GA) with the center of gravity crossover (CGX) and the minimal generation gap (MGG) model. With all genetic operations of GA including selection, crossover, mutation and evaluation implemented with NN modules, this approach can realize in parallel genetic operations on the whole chromosome to achieve the maximum parallel realization potential of the MGG model of the GA. At the same time expensive hardware for field programmable gate arrays (FPGA) and the high speed memory of hardware for GA can be avoided. The performance of our solution is validated with a suite of benchmark test functions. This paper suggests that implementing GA with NN is a promising research direction for greatly reducing the running time of GA.     

A neural network job-shop scheduler

This paper focuses on the development of a neural network (NN) scheduler for scheduling job-shops. In this hybrid intelligent system, genetic algorithms (GA) are used to generate optimal schedules to a known benchmark problem. In each optimal solution, every individually scheduled operation of a job is treated as a decision which contains knowledge. Each decision is modeled as a function of a set of job characteristics (e.g., processing time), which are divided into classes using domain knowledge from common dispatching rules (e.g., shortest processing time). A NN is used to capture the predictive knowledge regarding the assignment of operation’s position in a sequence. The trained NN could successfully replicate the performance of the GA on the benchmark problem. The developed NN scheduler was then tested against the GA, Attribute-Oriented Induction data mining methodology and common dispatching rules on a test set of randomly generated problems. The better performance of the NN scheduler on the test problem set compared to other methods proves the feasibility of NN-based scheduling. The scalability of the NN scheduler on larger problem sizes was also found to be satisfactory in replicating the performance of the GA.     

含离子液体体系的二元液液相平衡的计算

离子液体是许多领域中逐渐推广的绿色溶剂。鉴于离子液体在双相化学和工业萃取过程中的应用,对含离子液体体系的液液相平衡的研究,将有利于科研和生产。本文以文献中提供的未进行过数据关联的含[Et3NH][Al2Cl7]、[Emim] [NTf2]、[Bmim][PF6]、[Penmim][PF6]、[Hxmim][PF6]、[Hptmim][PF6]、[Omim][PF6]等7种离子液体与苯(C6C6),三氯化磷(PCl3),正丙醇(C3H7OH),正丁醇(C4H9OH),正戊醇(C5H11OH)等组成的10个二元系的溶解度数据为基础,应用NRTL和UNIQUAC活度系数模型进行了关联计算,得到了相应的模型参数。通过比较计算值与实验值,结果令人满意,NRTL和UNIQUAC方程计算得到的10个体系的全体平均误差(AAD,Overall-average deviation)的平均值分别为3．228％和2．691％。同时,在应用NRTL和UNIQUAC活度系数模型对含离子液体的二元体系作相平衡数据的关联的时候,首次将相互作用参数由常用的对温度的线性函数关系改进为对温度的双曲线型函数关系,从而显著地提高了对上部会溶点计算的准确性。     

Mutual neighbors and diagonal loading-based sparse locally linear embedding

In this study, a new Locally Linear Embedding (LLE) algorithm is proposed. Common LLE includes three steps. First, neighbors of each data point are determined. Second, each data point is linearly modeled using its neighbors and a similarity graph matrix is constructed. Third, embedded data are extracted using the graph matrix. In this study, for each data point mutual neighborhood conception and loading its covariance matrix diagonally are used to calculate the linear modeling coefficients. Two data points will be named mutual neighbors, if each of them is in the neighborhood of the other. Diagonal loading of the neighboring covariance matrix is applied to avoid its singularity and also to diminish the effect of noise in the reconstruction coefficients. Simulation results demonstrate the performance of applying mutual neighborhood conception and diagonal loading and their combination. Also, the results of applying the mutual neighborhood on Laplacian Eigenmap (LEM) demonstrate the good performance of the proposed neighbor selection method. Our proposed method improves recognition rate on Persian handwritten digits and face image databases.     

The inclusion of organic acids dissociation in the prediction of liquid-liquid equilibrium data

Advances in thermodynamic equilibrium have led to the recent development of binary, ternary and quaternary systems including electrolytes. However, a precise determination of phase equilibrium must consider the physical-chemical aspects of the components that will can to take place in a mixture. A solute is liable to dissociate hence the need to take this consideration into account. The chosen organic acids have attracted much attention in the research works but unfortunately, their dissociation into the aqueous phase has not been taken into the calculation of phase equilibrium. The present research work focuses on the development of the calculation of the phase equilibrium methods by including the dissociation of the chosen organic acids, for predicting the liquid-liquid equilibrium (LLE) data from the binary parameters for various ternary systems composed of {water-phosphoric acid-methylisobutylketone},{water-acetic acid-cumene},{water-propionic acid-cumene},{water-formic acid-isoamyl acetate} and {water-butyric acid-isoamyl acetate} at 298.15 K and normal atmospheric pressure, so as to be able to design electrolytes having specific properties. This requires a modeling framework that is based upon the fundamental laws and models of physics. Therefore, in our study, we used the Extended UNIQUAC model to predict the LLE data. We are newly investigating the effect of including the dissociation of the picked organic acids on prediction of phase equilibrium. The results have been compared with authors who consider no dissociation of organic acids, where they have been satisfactory.     

VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics.

In this letter, neural networks (NNs) classify alcoholics and nonalcoholics using features extracted from visual evoked potential (VEP). A genetic algorithm (GA) is used to select the minimum number of channels that maximize classification performance. GA population fitness is evaluated using fuzzy ARTMAP (FA) NN, instead of the widely used multilayer perceptron (MLP). MLP, despite its effective classification, requires long training time (on the order of 10(3) times compared to FA). This causes it to be unsuitable to be used with GA, especially for on-line training. It is shown empirically that the optimal channel configuration selected by the proposed method is unbiased, i.e., it is optimal not only for FA but also for MLP classification. Therefore, it is proposed that for future experiments, these optimal channels could be considered for applications that involve classification of alcoholics.     

无机盐-恒沸有机水溶液体系相平衡计算

针对无机盐-恒沸有机水溶液体系相平衡问题,提出用Gibbs自由能最小法计算。通过Pitzer理论与NRTL方程相结合来描述体系中各组分的活度系数,建立体系Gibbs自由能的计算模型,从而将相平衡计算转化为有约束的最小化问题,并应用遗传算法求解,进而得到体系平衡时的各相组成。以2个体系的相平衡计算为例,结果表明此方法可行有效,可为无机盐作盐析剂分离恒沸有机水溶液体系这一化工过程建立一定的理论基础和计算依据。     

Hybrid neural network and genetic algorithm based machining feature recognition

In this research, neural networks (NNs) and genetic algorithms (GAs) are used together in a hybrid approach to reduce the computational complexity of feature recognition problem. The proposed approach combines the characteristics of evolutionary technique and NN to overcome the shortcomings of feature recognition problem. Consideration is given to reduce the computational complexity of network with specific interest to design the optimum network architecture using GA input selection approach. In order to evaluate the performance of the proposed system, experimental results are compared with previous NN based feature recognition research.