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Blind source separation (BSS) involves recovering unobserved source signals from several mixed observations, typically obtained at the output of a set of sensors. Each sensor receives a different combination of the source signals. The adjective “blind” emphasizes the fact that: first, the source signals are not observed; and next, no information is available about the mixture. The assumption is often held physically that the source signals are mutually independent. Recently, BSS in signal processing has received considerable attention from researchers, due to its numerous promising applications in the areas of biomedical signal processing, digital communications and speech signal, sonar, image processing, and monitoring (Cichocki & Unbehauen, 1996), (Tangdiongga et al, 2001), (Yilmaz & Rickard, 2004), (Herault & Juten, 1986). A number of blind separation algorithms have been proposed based on different separation models. These algorithms play increasingly important roles in many applications. Since the pioneering work of Jutten and Herault (Herault & Juten, 1986), a variety of algorithms have been proposed for BSS. In general, the existing algorithms can be divided into five major categories: neural network-based algorithms (Cichocki & Unbehauen, 1996), (Zhang & Kassam, 2004), density model-based algorithms (Amari et al, 1997), (Lee et al, 1999a), algebraic algorithms (Belouchrani et al, 1997), (Li & Wang, 2002), information-theoretic algorithms (Pajunen, 1998), (Pham & Vrins, 2005) and space-based algorithms (Yilmaz & Rickard, 2004), (Lee et al, 1999b). A source signal with sparse representation means that at most one value of the signal isn’t zero at an instant, making the vector of sensor signals (mixtures) equivalent to some mixing vector. Therefore, the sparse-based BSS problem could be solved by searching for mixing vectors; moreover, recovering source signals. Like the mixtures with sparse representation, each base vectors of the unknown mixing matrix will be displayed on a 2-D plane coordinate as a directional line when two sensors are used. The sparse representation is first introduced in underdetermined BSS by Lee et al. (Lee et al, 1999b). After its introduction, several related methods have been proposed continuously for solving underdetermined BSS

Selecting important genes from microarray data is a considerably challenging problem as shown in Guyon's 2002 paper in this journal. We have developed an alternative feature ranking and selection methodology to tackle this problem. On the basis of several cancer data sets, AMFES (adaptive multiple features selection) outperforms Guyon's RFE (recursive feature elimination). In this paper, we will present a comprehensive and systematic comparison of three methods: AMFES, RFE, and the CORR (correlation coefficient) on five data sets (leukemia, colon, lymphoma, prostate, and potentially others). The leukemia, colon, and lymphoma data sets are adapted from Guyon's paper for convenience and the prostate cancer data set is from a public database, NCBI GEO (Gene Expression Omnibus). These three methods are compared in terms of test accuracy, number of selected features, computational time (total and training), statistical significance (t test, p values, and ROC (receiver operating characteristic)/AUC (area under curve)), and the discovery rate of informative features. AMFES obtains better results in computational time and number of selected features while maintaining higher or comparable test accuracy, statistical significance, and the discovery rate of informative features. In addition, AMFES can serve as a general methodology for other similar problems such as sampling and data mining.

This paper proposes an intelligent flight task algorithm for unmanned aerial vehicles (UAV) to effectively determine and search the best flight routes in three-dimensional and complicated topographies, where the topography data includes the height of the object on the earth and the aerial imagery map of the earth is obtained by the Satellite. The proposed method is composed mainly of the particle swarm optimizer (PSO), skeletonization, and B-spline curve. Skeletonization is regarded as a pre-processing procedure for topography data to reduce complexity of the searching space and inhibit aimless searches of solutions. B-spline curve method is adopted to provide a smooth flight route for UAV. The best control points of a B-spline curve are determined by PSO. In this paper, the initial population of PSO is acquired through skeletonization to improve the efficiency of the search for the best flight route. The results of the simulation demonstrate the abilities of the proposed method to provide the best flight routes efficiently for UAV.

This paper proposes an improved multi-objective particle swarm optimizer with proportional distribution and jump improved operation, named PDJI-MOPSO, for dealing with multi-objective problems. PDJI-MOPSO maintains diversity of new found non-dominated solutions via proportional distribution, and combines advantages of wide-ranged exploration and extensive exploitations of PSO in the external repository with the jump improved operation to enhance the solution searching abilities of particles. Introduction of cluster and disturbance allows the proposed method to sift through representative non-dominated solutions from the external repository and prevent solutions from falling into local optimum. Experiments were conducted on eight common multi-objective benchmark problems. The results showed that the proposed method operates better in five performance metrics when solving these benchmark problems compared to three other related works.

In this paper, a sharing evolution genetic algorithms (SEGA) is proposed to solve various global numerical optimization problems. The SEGA employs a proposed population manager to preserve chromosomes which are superior and to eliminate those which are worse. The population manager also incorporates additional potential chromosomes to assist the solution exploration, controlled by the current solution searching status. The SEGA also uses the proposed sharing concepts for cross-over and mutation to prevent populations from falling into the local minimal, and allows GA to easier find or approach the global optimal solution. All the three parts in SEGA, including population manager, sharing cross-over and sharing mutation, can effective increase new born offspring's solution searching ability. Experiments were conducted on CEC-05 benchmark problems which included unimodal, multi-modal, expanded, and hybrid composition functions. The results showed that the SEGA displayed better performance when solving these benchmark problems compared to recent variants of the genetic algorithms.

A difficult blind source separation (BSS) issue dealing with an unknown and dynamic number of sources is tackled in this study. In the past, the majority of BSS algorithms familiarize themselves with situations where the numbers of sources are given, because the settings for the dimensions of the algorithm are dependent on this information. However, such an assumption could not be held in many advanced applications. Thus, this paper proposes the adaptive neural algorithm (ANA) which designs and associates several auto-adjust mechanisms to challenge these advanced BSS problems. The first implementation is the on-line estimator of source numbers improved from the cross-validation technique. The second is the adaptive structure neural network that combines feed-forward architecture and the self-organized criterion. The last is the learning rate adjustment in order to enhance efficiency of learning. The validity and performance of the proposed algorithm are demonstrated by computer simulations, and are compared to algorithms with state of the art. From the simulation results, these have been confirmed that the proposed ANA performed better separation than others in static BSS cases and is feasible for dynamic BSS cases.

The particle swarm optimizer (PSO) is a population-based optimization technique that can be applied to a wide range of problems. This paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for PSO (EPUS-PSO), adopting a population manager to significantly improve the efficiency of PSO. This is achieved by using variable particles in swarms to enhance the searching ability and drive particles more efficiently. Moreover, sharing principals are constructed to stop particles from falling into the local minimum and make the global optimal solution easier found by particles. Experiments were conducted on unimodal and multimodal test functions such as Quadric, Griewanks, Rastrigin, Ackley, and Weierstrass, with and without coordinate rotation. The results show good performance of the EPUS-PSO in solving most benchmark problems as compared to other recent variants of the PSO.

This paper focuses on blind source separation with an unknown number of sources, which is the case generally assumed in most practical applications. Several over-determined neural algorithms (more sensors m than sources n) have been proposed to solve the problems associated with these cases, but separating performance is often sacrificed in order to prevent divergence. The general natural gradient descent can be validly applied to determined algorithms (m=n) only. Therefore, to better solve the problems, an algorithm associating the feed-forward neural network and an auto-trimming technique is proposed. The learning process starts with an over-determined architecture, followed by two steps used in every iteration. First, the number of sources is estimated by using the stability discriminant function, next, the neural network gradually trims redundant nodes according to an instant estimation. Validity and performance of the proposed approaches are demonstrated with computer simulations on artificially synthesized signals and compared with the well-known algorithm proposed by Ye et al.

This paper presents a macrocell placement constraint and overlap removal methodology using an improved particle swarm optimization (PSO). Several techniques have been proposed to improve PSO, such as methods to prevent the floorplan from falling into the local minimum and to assist in finding the global minimum. The proposed method can deal with various kinds of placement constraints and can process them simultaneously. Experiments employing MCNC and GSRC benchmarks show the difference in the efficiency and robustness of proposed method in the exploration for more optimal solutions through restricted placement and overlap removal compared with other methods. The proposed approach exhibits rapid convergence and leads to more optimal solutions than other related approaches; furthermore, it displays efficient packing with all the constraints satisfied.

In an attempt to build a good predictor of the performance of survey interviewers, we propose a feature selection method that derives the featuresi¦ strength (i.e., degree of usefulness) from various feature subsets drawn from a pool of all the features. The method also builds a predictor by using support vector regression (SVR) as the learning machine and the selected features as variables. Applying the method to a collection of 278 instances obtained from 67 interviewers par-ticipating in eight survey projects, we identified three critical features, experience and two attributional style variables, out of fifteen features. Compared with results of four existing methods, the proposed predictor produced the smallest predictive error. Furthermore, the three features utilized by our method were also identified as the most important features by the four compared methods.

To handle problems created by large data sets, we propose a method that uses a decision tree to decompose a given data space and trains SVMs on the decomposed regions. Although there are other means of decomposing a data space, we show that the decision tree has several merits for large-scale SVM training. First, it can classify some data points by its own means, thereby reducing the cost of SVM training applied to the remaining data points. Second, it is efficient for seeking the parameter values that maximize the validation accuracy, which helps maintain good test accuracy. For experiment data sets whose size can be handled by current non-linear, or kernel-based, SVM training techniques, the proposed method can speed up the training by a factor of thousands, and still achieve comparable test accuracy.

Radial Basis Function neural network (RBFNN) is a combination of learning vector quantizer LVQ-I and gradient descent. RBFNN is first proposed by (Broomhead & Lowe, 1988), and their interpolation and generalization properties are thoroughly investigated in (Lowe, 1989), (Freeman & Saad, 1995). Since the mid-1980s, RBFNN has been used to apply on many applications, such as pattern classification, system identification, nonlinear function approximation, adaptive control, speech recognition, and time-series prediction, and so on. In contrast to the well-known Multilayer Perceptron (MLP) Networks, the RBF network utilizes a radial construction mechanism. MLP were trained by the error Back Propagation (BP) algorithm, since the RBFNN has a faster training procedure substantially and adopts typical two-stage training scheme, it can avoid solution to fall into local optima. A key point of RBFNN is to decide a proper number of hidden nodes. If the hidden node number of RBFNN is too small, the generated output vectors may be in low accuracy. On the contrary, it with too large number of hidden nodes may cause over-fitting for the input data, and influences global generalization performance. In conventional RBF training approach, the number of hidden node is usually decided according to the statistic properties of input data, then determine the centers and spread width for each hidden nodes by means of k-means clustering algorithm (Moddy & Darken, 1989). The drawback of this approach is that the network performance is depended on the pre-selected number of hidden nodes. If an unsuitable number is chosen, RBFNN may present a poor global generalization capability, as slow training speed, and requirement for large memory space. To solve this problem, the self-growing RBF techniques were proposed in (Karayiannis & Mi, 1997), (Zheng et al, 1999). However, the predefined parameters and local searching on solution space cause the inaccuracy of approximation from a sub-solution. Evolutionary computation is a globally optimization technique, where the aim is to improve the ability of individual to survive. Among that, Genetic Algorithm (GA) is a parallel searching technique that mimics natural genetics and the evolutionary process. In (Back et al, 1997), they employed GA to determine the RBFNN structure so the optimal number and distribution of RBF hidden nodes can be obtained automatically. A common approach is applied GA to search for the optimal network structure among several candidates

In multi-objective particle swarm optimization (MOPSO) methods, selecting the best local guide (the global best particle) for each particle of the population provides great benefits on the convergence and diversity of solutions, especially when problems are optimized with a large number of objectives. This paper introduces the proportional distribution based particle swarm optimizer (PSO) with cross-over operation in external repository for dealing with multi-objective problems. It combines advantages of wide-ranged exploration with cross-over operation, to maintain diversity of new found non-dominated solutions via proportional distribution in the external repository, and deep exploitations of PSO to enhance the solution searching abilities of particles. Introduction of cluster and disturbance allows the proposed method to sift through representative non-dominated solutions from the external repository and prevent solutions from falling into the local optimal. Experiments were conducted on four common MO benchmark problems. The results showed that the proposed method accomplishes better in four performance metrics when solving these benchmark problems compared to three other related works.

In this paper, a sharing evolution genetic algorithms (SEGA) is proposed to solve global numerical optimization problems. In the SEGA, three strategies are proposed, which are population manager, sharing cross-over and sharing mutation, for effective increasing new born offspring's solution searching ability. Experiments were conducted on CEC-05 benchmark problems which included unimodal, multimodal, expanded, and hybrid composition functions. The results showed that the proposed method exhibits better performance when solving these benchmark problems compared to recent variants of the genetic algorithms.

This paper discusses the improvement of premature convergence in genetic algorithm (GA) used for optimizing multimodal numerical problems. Mutation is the principle operation in GA for enhancing the degree of population diversity, but is not efficient often, particularly in traditional GA. Moreover, the definition of mutation rate is a tradeoff between computing time and accuracy. In our work, we introduce the cluster method nearest neighborhood for estimating population diversity. According to this estimation, the mutation rate is adaptively given and repeat chromosomes are discarded over evolution. Consequently, the proposed cluster-based GA can choose a suitable mutation number for reducing computing time and maintain the population variety for preventing premature convergence. It is confirmed in numerical optimization simulations that the proposed GA is superior than traditional GA used fixed mutation rate in terms of accuracy, computing time and convergent speed.

A self-organized rule is proposed to process the separation of unknown number of sources of blind signal. The rule is applied to the feed-forward neural network (FFNN) which is based on a mean weighting gradient (MWG) function. The algorithms adjust the architecture of neural network. For both, the performances are the mixture of node number and MWG threshold /spl xi/.The separating number of sources estimated accurately by experiment of computer simulations.