31045346
A New Fuzzy Identification Method Based on Adaptive Critic Designs
A new fuzzy identification method for unknown nonlinear discrete systems is presented by introducing adaptive critic designs into the generalized fuzzy hyperbolic model (GFHM). This method minimizes the long-time error other than the immediate error to improve the identification effect. We first represent the GFHM with a neural network structure, and then utilize the adaptive critic designs (ACDs) to get the optimal parameters of the network so that the long-time identification error is minimized. Finally, we give a simulation example to verify the effectiveness of this identification method.
5450384
Gradient-based methods for simultaneous blind separation of mixed source signals
This paper presents gradient-based methods for simultaneous blind separation of arbitrarily mixed source signals. We consider the regular case where the mixing matrix has full column rank as well as ill-conditioned cases. Two cost functions based on fourth-order cumulants are introduced to simultaneously separate all separable single sources and all inseparable mixtures. By minimizing the cost functions, two gradient-based methods are developed. Our algorithms derived from gradient-based methods are guaranteed to converge. Finally, simulation results show the effectiveness of our methods.
22528927
Solving the N-bit parity problem using neural networks
In this letter, a constructive solution to the N-bit parity problem is provided with a neural network that allows direct connections between the input layer and the output layer. The present approach requires no training and adaptation, and thus it warrants the use of the simple threshold activation function for the output and hidden layer neurons. It is previously shown that this choice of activation function and network structure leads to several solutions for the 3-bit parity problem obtained using linear programming. One of the solutions for the 3-bit parity problem is then generalized to obtain a solution for the N-bit parity problem using left floor N/2 right floor hidden layer neurons. It is shown that through the choice of a "staircase" type activation function, the left floor N/2 right floor hidden layer neurons can be further combined into a single hidden layer neuron.
38948507
Analysis and synthesis of a class of neural networks with sparse interconnections
The authors present results for the analysis and synthesis of a class of neural networks with sparse or partial interconnecting structure. The, design procedure guarantees to synthesize neural networks with arbitrarily predetermined sparse interconnection structures and to store any given set of desired bipolar patterns as memories. It is shown that a sufficient condition for the existence of a design for neural networks with sparse interconnection is self-feedback for every neuron in the network. Several specific examples are included to demonstrate the applicability of the methodology.<<ETX>>
7951709
Design of bidirectional associative memories based on the perceptron training technique
Bidirectional associative memories are being used extensively for solving a variety of problems related to pattern recognition. In the present paper, a new synthesis approach is developed for bidirectional associative memories using feedback neural networks. The synthesis problem of bidirectional associative memories is formulated as a set of linear inequalities which can be solved using the perceptron training algorithm. To demonstrate the applicability of the present results a specific example is considered.
38675279
On-Line Learning Control for Discrete Nonlinear Systems Via an Improved ADDHP Method
This paper mainly discusses a generic scheme for on-line adaptive critic design for nonlinear system based on neural dynamic programming (NDP), more exactly, an improved action-depended dual heuristic dynamic programming (ADDHP) method. The principal merit of the proposed method is to avoid the model neural network which predicts the state of next time step, and only use current and previous states in the method, as makes the algorithm more suitable for real-time or on-line application for process control. In this paper, convergence proof of the method will also be given to guarantee the control to reach the optimal. At last, simulation result verifies the performance.
694856
Support Concurrent Queries in Multiuser CBIR Systems
Various techniques have been developed for different query types in content-based image retrieval (CBIR) systems such as sampling queries, constrained sampling queries, multiple constrained sampling queries, k-NN queries, constrained k-NN queries, and multiple localized k-NN queries. In this paper, we propose a generalized query model suitable for expressing queries of different types, and investigate efficient processing techniques for this new framework. We develop new storage and query processing techniques to exploit sequential access and leverage inter-query concurrency to share computation. Our experimental results, based on the Corel dataset, indicates that the proposed optimization can significantly reduce average response time in a multiuser environment.
769378
Efficient Distribution of Key Chain Commitments for Broadcast Authentication in Distributed Sensor Networks
Broadcast authentication is a fundamental security service in distributed sensor networks. A scheme named $\mu$TESLA has been proposed for efficient broadcast authentication in such networks. However, $\mu$TESLA requires initial distribution of certain information based on unicast between the base station and each sensor node before the actual authentication of broadcast messages. Due to the limited bandwidth in wireless sensor networks, this initial unicast-based distribution severely limits the application of $\mu$TESLA in large sensor networks. This paper presents a novel technique to replace the unicast-based initialization with a broadcast-based one. As a result, $\mu$TESLA can be used in a sensor network with a large amount of sensors, as long as the message from the base station can reach these sensor nodes. This paper further explores several techniques that improve the performance, the robustness, as well as the security of the proposed method. The resulting protocol satisfies several nice properties, including low overhead, tolerance of message loss, scalability to large networks, and resistance to replay attacks as well as some known Denial of Service (DOS) attacks.
53748903
A Framework for Designing Reusable Analog Circuits
215202
Neural network-based model reference adaptive control system
In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.
1435851
Soft shape context for iterative closest point registration
This paper introduces a shape descriptor, the soft shape context, motivated by the shape context method. Unlike the original shape context method, where each image point was hard assigned into a single histogram bin, we instead allow each image point to contribute to multiple bins, hence more robust to distortions. The soft shape context can easily be integrated into the iterative closest point (ICP) method as an auxiliary feature vector, enriching the representation of an image point from spatial information only, to spatial and shape information. This yields a registration method more robust than the original ICP method. The method is general for 2D shapes. It does not calculate derivatives, hence being able to handle shapes with junctions and discontinuities. We present experimental results to demonstrate the robustness compared with the standard ICP method.