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.
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.
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.
31887438
Robust Stability Analysis of a Class of Hopfield Neural Networks with Multiple Delays
The robust stability of a class of Hopfield neural networks with multiple delays is analyzed. Sufficient conditions for the global robust stability of the equilibrium point are established through constructing a suitable Lyapunov-Krasovskii functional. The present results take the form of linear matrix inequalities, and are computationally efficient. In addition, the results are independent of delays and established without assuming differentiability and monotonicity of the activation function.
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.
35821065
Criteria for asymptotic stability of a class of discrete systems with multiple independent variables
This paper investigates the problem of asymptotic stability for a class of linear shift-invariant discrete systems with multiple independent variables. We first establish the equivalence of this problem and the problem of robust stability for a class of ordinary linear shift-varying discrete systems with the matrix uncertainty set defined by the coefficient matrices of the original system. On the basis of this equivalence, by using the variational method and the Lyapunov Second Method, necessary and sufficient conditions for asymptotic stability are obtained in different forms for the class of systems considered in the present paper. The parametric classes of Lyapunov functions which define the necessary and sufficient conditions of asymptotic stability are determined. We use the piecewise linear polyhedral Lyapunov functions of the infinity vector norm type to derive an algebraic criterion for asymptotic stability of the given class of discrete systems in the form of solvability conditions of a set of matrix equations.
84347
Latent Layout Analysis for Discovering Objects in Images
Latent layout analysis (LLA) is a novel unsupervised learning technique to discover objects in unseen images using a set of un-annotated training images. LLA defines a generative model that associates latent aspects to local appearances. The dependency between aspects and position is captured by a spatial sensitive aspect model. This dependency distinguishes LLA from probabilistic latent semantic analysis (PLSA). The latent aspects together with the latent layout constitute a compact scene representation. We demonstrate that the proposed LLA significantly outperforms probabilistic latent semantic analysis in two tasks: object discovery (detection) and object localization
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.
10993158
Efficient and Distributed Access Control for Sensor Networks
Sensor networks are often used to sense the physical world and provide observations for various uses. In hostile environments, it is critical to control the network access to ensure the integrity, availability, and at times confidentiality of the sensor data. This paper develops efficient methods for distributed access control in sensor networks. The paper starts with a baseline approach, which provides a more flexible and efficient way to enforce access control when compared with previous solutions. This paper then extends the baseline approach to enable privilege delegation, which allows a user to delegate its privilege to other users without using a trusted server, and broadcast query, which allows a user to access the network at a large scale efficiently. The privilege delegation and broadcast query are very useful in practice; none of the current solutions can achieve these two properties.
286417
Practical broadcast authentication in sensor networks
Broadcast authentication is a critical security service in sensor networks; it allows a sender to broadcast messages to multiple nodes in an authenticated way. /spl mu/TESLA and multi-level /spl mu/TESLA have been proposed to provide such services for sensor networks. However, none of these techniques are scalable in terms of the number of senders. Though multi-level /spl mu/TESLA schemes can scale up to large sensor networks (in terms of receivers), they either use substantial bandwidth and storage at sensor nodes, or require significant resources at senders to deal with DOS attacks. This paper presents efficient techniques to support a potentially large number of broadcast senders using /spl mu/TESLA instances as building blocks. The proposed techniques are immune to the DOS attacks. This paper also provides two approaches, a revocation tree based scheme and a proactive distribution based scheme, to revoke the broadcast authentication capability from compromised senders. The proposed techniques are implemented, and evaluated through simulation on TinyOS. The analysis and experiment show that these techniques are efficient and practical, and can achieve better performance than the previous approaches.