18621072
Primal and dual neural networks for shortest-path routing
Presents two recurrent neural networks for solving the shortest path problem. Simplifying the architecture of a recurrent neural network based on the primal problem formulation, the first recurrent neural network called the primal routing network has less complex connectivity than its predecessor. Based on the dual problem formulation, the second recurrent neural network called the dual routing network has even simpler architecture. While being simple in architecture, the primal and dual routing networks are capable of shortest-path routing like their predecessor.
16046090
Nonlinear blind source separation using higher order statistics and a genetic algorithm
This paper presents a novel method for blindly separating unobservable independent source signals from their nonlinear mixtures. The demixing system is modeled using a parameterized neural network whose parameters can be determined under the criterion of independence of its outputs. Two cost functions based on higher order statistics are established to measure the statistical dependence of the outputs of the demixing system. The proposed method utilizes a genetic algorithm (GA) to minimize the highly nonlinear and nonconvex cost functions. The GA-based global optimization technique is able to obtain superior separation solutions to the nonlinear blind separation problem from any random initial values. Compared to conventional gradient-based approaches, the GA-based approach for blind source separation is characterized by high accuracy, robustness, and convergence rate. In particular, it is very suitable for the case of limited available data. Simulation results are discussed to demonstrate that the proposed GA-based approach is capable of separating independent sources from their nonlinear mixtures generated by a parametric separation model.
18182406
Recurrent neural networks for minimum infinity-norm kinematic control of redundant manipulators
This paper presents two neural network approaches to minimum infinity-norm solution of the velocity inverse kinematics problem for redundant robots. Three recurrent neural networks are applied for determining a joint velocity vector with its maximum absolute value component being minimal among all possible joint velocity vectors corresponding to the desired end-effector velocity. In each proposed neural network approach, two cooperating recurrent neural networks are used. The first approach employs two Tank-Hopfield networks for linear programming. The second approach employs two two-layer recurrent neural networks for quadratic programming and linear programming, respectively. Both the minimal 2-norm and infinity-norm of joint velocity vector can be obtained from the output of the recurrent neural networks. Simulation results demonstrate that the proposed approaches are effective with the second approach being better in terms of accuracy and optimality.
8287323
A recurrent neural network for solving linear projection equations
Linear projection equations arise in many optimization problems and have important applications in science and engineering. In this paper, we present a recurrent neural network for solving linear projection equations in real time. The proposed neural network has two layers and is amenable to parallel implementation with simple hardware. In the theoretical aspect, we prove that the proposed neural network can converge globally to the solution set of the problem when the matrix involved in the problem is positive semidefinite and can converge exponentially to a unique solution when the matrix is positive definite. In addition, we analyze the stability of the related dynamic system in detail. As an application, we show that the proposed neural network can be used directly to solve linear and convex quadratic programming problems and linear complementary problems with positive semidefinite matrices. The validity and transient behavior of the neural network are demonstrated by using three numerical examples.
18821545
Multiperiodicity and Exponential Attractivity Evoked by Periodic External Inputs in Delayed Cellular Neural Networks
We show that an n-neuron cellular neural network with time-varying delay can have 2n periodic orbits located in saturation regions and these periodic orbits are locally exponentially attractive. In addition, we give some conditions for ascertaining periodic orbits to be locally or globally exponentially attractive and allow them to locate in any designated region. As a special case of exponential periodicity, exponential stability of delayed cellular neural networks is also characterized. These conditions improve and extend the existing results in the literature. To illustrate and compare the results, simulation results are discussed in three numerical examples.
6599744
A multilayer recurrent neural network for solving continuous-time algebraic Riccati equations
A multilayer recurrent neural network is proposed for solving continuous-time algebraic matrix Riccati equations in real time. The proposed recurrent neural network consists of four bidirectionally connected layers. Each layer consists of an array of neurons. The proposed recurrent neural network is shown to be capable of solving algebraic Riccati equations and synthesizing linear-quadratic control systems in real time. Analytical results on stability of the recurrent neural network and solvability of algebraic Riccati equations by use of the recurrent neural network are discussed. The operating characteristics of the recurrent neural network are also demonstrated through three illustrative examples.
54136036
Region-based retrieval of biomedical images
14083792
Multicarrier CDMA overlay for ultra-wideband communications
In this letter, the performance of multicarrier code-division multiple-access (CDMA) systems is studied in the presence of narrowband interference for future ultra-wideband (UWB) communications. A Nakagami fading channel is assumed, and notch filters along with diversity techniques are used in the multicarrier CDMA receiver. A complete performance analysis of error probability is given. It is shown that when the number of subcarriers jammed by narrowband interference is small, the multicarrier receiver without notch filters can work well, due to the gain of frequency diversity from nonjammed subcarriers. On the other hand, when the number of subcarriers jammed by the narrowband interference is large, using notch filters can improve the multicarrier system performance significantly.
9285084
Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data
BackgroundUsing DNA microarrays, we have developed two novel models for tumor classification and target gene prediction. First, gene expression profiles are summarized by optimally selected Self-Organizing Maps (SOMs), followed by tumor sample classification by Fuzzy C-means clustering. Then, the prediction of marker genes is accomplished by either manual feature selection (visualizing the weighted/mean SOM component plane) or automatic feature selection (by pair-wise Fisher's linear discriminant).ResultsThe proposed models were tested on four published datasets: (1) Leukemia (2) Colon cancer (3) Brain tumors and (4) NCI cancer cell lines. The models gave class prediction with markedly reduced error rates compared to other class prediction approaches, and the importance of feature selection on microarray data analysis was also emphasized.ConclusionsOur models identify marker genes with predictive potential, often better than other available methods in the literature. The models are potentially useful for medical diagnostics and may reveal some insights into cancer classification. Additionally, we illustrated two limitations in tumor classification from microarray data related to the biology underlying the data, in terms of (1) the class size of data, and (2) the internal structure of classes. These limitations are not specific for the classification models used.
2685835
Energy Consumption of Swarm Intelligence inspired Routing Algorithms in MANETs
Swarm Intelligence (SI) inspired routing algorithms have become a research focus in recent years due to their self-organizing nature, which is very suitable to the routing problems in Mobile Ad hoc Networks (MANETs). The network information can be collected and updated in a decentralized and dynamic way through the localized cooperation among these swarm agents. In this paper, we, for the first time, provide a comprehensive comparison about current SI inspired routing algorithms in MANETs in the aspects of network metrics and simulation environment. Then, we make a detailed study about the energy consumption of the SI inspired routing problem in MANETs. Based on this study, we present the selection criterion of the intermediate nodes so as to be energy efficient. Experimental results are provided with reasonable verification.
11111374
Neural Network Based Stereotyping for User Profiles
This paper discusses an approach of establishing system models of users’ task related characteristics, such as domain knowledge in human-computer interaction. Several neural networks are tested for the modelling process. These networks function as associative memories that capture the causal relationships among assumptions about the users’ characteristics. The outputs from the networks are considered as stereotypes assigned to individual users. It is suggested that this approach can be expected to overcome some limitations of user modelling approaches in terms of pattern recognition and classification.