12016558
Constrained motion control of flexible robot manipulators based on recurrent neural networks
In this paper, a neural network approach is presented for the motion control of constrained flexible manipulators, where both the contact force everted by the flexible manipulator and the position of the end-effector contacting with a surface are controlled. The dynamic equations for vibration of flexible link and constrained force are derived. The developed control, scheme can adaptively estimate the underlying dynamics of the manipulator using recurrent neural networks (RNNs). Based on the error dynamics of a feedback controller, a learning rule for updating the connection weights of the adaptive RNN model is obtained. Local stability properties of the control system are discussed. Simulation results are elaborated on for both position and force trajectory tracking tasks in the presence of varying parameters and unknown dynamics, which show that the designed controller performs remarkably well.
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.
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.
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.
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.
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.
2511396
An architecture for creating collaborative semantically capable scientific data sharing infrastructures
Increasingly, scientists are seeking to collaborate and share data among themselves. Such sharing is can be readily done by publishing data on the World-Wide Web. Meaningful querying and searching on such data depends upon the availability of accurate and adequate metadata that describes the data and the sources of the data. In this paper, we outline the architecture of an implemented cyber-infrastructure for chemistry that provides tools for users to upload datasets and their metadata to a database. Our proposal combines a two level metadata system with a centralized database repository and analysis tools to create an effective and capable data sharing infrastructure. Our infrastructure is extensible in that it can handle data in different formats and allows different analytic tools to be plugged in.
206541019
An online self-organizing neuro-fuzzy control for autonomous underwater vehicles
Controlling autonomous underwater vehicles (AUVs) in an uncertain and unstructured environment presents many challenging control problems. Model-based control strategies have been used with limited success. The paper presents an online self-organizing neuro-fuzzy control that serves as a better alternative control scheme in controlling AUVs. The proposed self-organizing neuro-fuzzy controller is a six-layer feedforward neural network that is capable of self-constructing and self-restructuring its internal node connectivity and learning the parameters of each node based on incoming training data. Computer simulations have been conducted to validate the performance of the proposed neuro-fuzzy controller and an experimental verification has been scheduled to verify if on ODIN, an autonomous underwater vehicle developed at the University of Hawaii.
7340804
Cooperative proxy caching for wireless base stations
This paper proposes a mobile cache model to facilitate the cooperative proxy caching in wireless base stations. This mobile cache model uses a network cache line to record the caching state information about a web document for effective data search and cache space management. Based on the proposed mobile cache model, a P2P cooperative proxy caching scheme is proposed to use a self-configured and self-managed virtual proxy graph (VPG), independent of the underlying wireless network structure and adaptive to the network and geographic environment changes, to achieve efficient data search, data cache and date replication. Based on demand, the aggregate effect of data caching, searching and replicating actions by individual proxy servers automatically migrates the cached web documents closer to the interested clients. In addition, a cache line migration (CLM) strategy is proposed to flow and replicate the heads of network cache lines of web documents associated with a moving mobile host to the new base station during the mobile host handoff. These replicated cache line heads provide direct links to the cached web documents accessed by the moving mobile hosts in the previous base station, thus improving the mobile web caching performance. Performance studies have shown that the proposed P2P cooperative proxy caching schemes significantly outperform existing caching schemes.
28415004
Scientific Data Mining: A Case Study
Scientific data mining is the activity of finding significant information in scientific data. This paper presents an example of scientific data mining: the discovery of approximately common patterns in RNA secondary structures. We represent an RNA secondary structure by an ordered labeled tree based on a previously proposed scheme. The patterns in the trees are substructures that can differ in both substitutions and deletions/insertions of nodes of the trees. Our techniques incorporate approximate tree matching algorithms and novel heuristics for discovery and optimization. Experimental results obtained by running these algorithms on both generated data and RNA secondary structures show the good performance of the algorithms. It is shown that the optimization heuristics speed up the discovery algorithm by a factor of 10. Moreover, our optimized approach is 100,000 times faster than the brute force method.
2659130
Self-adaptive recurrent neuro-fuzzy control for an autonomous underwater vehicle
This paper presents the utilization of a self-adaptive recurrent neuro-fuzzy control as a feedforward controller and a proportional-plus-derivative (PD) control as a feedback controller for controlling an autonomous underwater vehicle (AUV) in an unstructured environment. Without a priori knowledge, the recurrent neuro-fuzzy system is first trained to model the inverse dynamics of the AUV and then it utilized as a feedforward controller to compute the nominal torque of the AUV along a desired trajectory. The PD feedback controller computes the error torque to minimize the system error along the desired trajectory. This error torque also provides an error signal for online updating the parameters in the recurrent neuro fuzzy control to adapt in a changing environment. A systematic self-adaptive learning algorithm, consisting of a mapping-constrained agglomerative clustering algorithm for the structure learning and a recursive recurrent learning algorithm for the parameter learning, was developed to construct the recurrent neuro-fuzzy system to model the inverse dynamics of an AUV with fast learning convergence. Computer simulations of the proposed recurrent neuro-fuzzy control scheme and its performance comparison with an adaptive controller were conducted to validate the effectiveness of the proposed approach.