Haixiang Shi

A gradual noisy chaotic neural network for solving the broadcast scheduling problem in packet radio networks

In this paper, we propose a gradual noisy chaotic neural network (G-NCNN) to solve the NP-complete broadcast scheduling problem (BSP) in packet radio networks. The objective of the BSP is to design an optimal time-division multiple-access (TDMA) frame structure with minimal TDMA frame length and maximal channel utilization. A two-phase optimization is adopted to achieve the two objectives with two different energy functions, so that the G-NCNN not only finds the minimum TDMA frame length but also maximizes the total node transmissions. In the first phase, we propose a G-NCNN which combines the noisy chaotic neural network (NCNN) and the gradual expansion scheme to find a minimal TDMA frame length. In the second phase, the NCNN is used to find maximal node transmissions in the TDMA frame obtained in the first phase. The performance is evaluated through several benchmark examples and 600 randomly generated instances. The results show that the G-NCNN outperforms previous approaches, such as mean field annealing, a hybrid Hopfield network-genetic algorithm, the sequential vertex coloring algorithm, and the gradual neural network.

Noisy Chaotic Neural Networks With Variable Thresholds for the Frequency Assignment Problem in Satellite Communications

We propose a novel approach, i.e., a noisy chaotic neural network with variable thresholds (NCNN-VT), to solve the frequency assignment problem in satellite communications. The objective of this NP-complete optimization problem is to minimize cochannel interference between two satellite systems by rearranging frequency assignments. The NCNN-VT model consists N times M of noisy chaotic neurons for an N-carrier M-segment problem. The NCNN-VT facilitates the interference minimization by mapping the objective to variable thresholds (biases) of the neurons. The performance of the NCNN-VT is demonstrated by solving a set of benchmark problems and randomly generated test instances. The NCNN-VT achieves better solutions, i.e., smaller interference with much lower computation cost compared to existing algorithms.

Delay-Constrained Multicast Routing Using the Noisy Chaotic Neural Networks

We present a method to compute the delay constrained multicast routing tree by employing chaotic neural networks. Experimental result shows that the noisy chaotic neural network (NCNN) provides optimal solution more often compared to the transiently chaotic neural network (TCNN) and the Hopfield neural network (HNN). Furthermore, compared with the bounded shortest multicast algorithm (BSMA), the noisy chaotic neural network is able to find multicast trees with lower cost.

Cellular Neural Networks With Transient Chaos

A new model of cellular neural networks (CNNs) with transient chaos is proposed by adding negative self-feedbacks into CNNs after transforming the dynamic equation to discrete time via Eulers method. The simulation on the single neuron model shows stable fix points, bifurcation and chaos. Hence, this new CNN model has richer and more flexible dynamics, and therefore may possess better capabilities of solving various problems, compared to the conventional CNN with only stable dynamics

A hybrid neural network for optimal TDMA transmission scheduling in packet radio networks

In this paper we propose a hybrid method to solve the broadcast scheduling problem in packet radio networks. In the first stage, we use a backtracking sequential coloring algorithm to obtain a minimal TDMA frame length and the corresponding transmission assignments. In the second stage, we employ the noisy chaotic neural network to find the maximum node transmission based on the results obtained in the previous stage. Simulation results show that this hybrid method outperforms previous approaches, such as mean field annealing, a hybrid of the Hopfield neural network and genetic algorithms, the sequential vertex coloring algorithm, and the gradual neural network.

Minimizing Interference in Satellite Communications Using Chaotic Neural Networks

We solve the frequency assignment problem (FAP) in satellite communications with transiently chaotic neural networks (TCNN). The objective of this optimization problem is to minimize cochannel interference between two satellite systems by rearranging the frequency assignments. For an N-carrier-M-segment FAP problem, the TCNN consists of N x M neurons. The performance of the TCNN is demonstrated through solving a set of benchmark problems, where the TCNN finds comparative if not better solutions compared to the existing algorithms.

Chaotic cellular neural networks with negative self-feedback

We propose a new model of Chaotic Cellular Neural Networks (C-CNNs) by introducing negative self-feedback into the Euler approximation of the continuous CNNs. According to our simulation result for the single neuron model, this new C-CNN model has richer and more flexible dynamics, compared to the conventional CNN with only stable dynamics. The hardware implementation of this new network may be important for solving a wide variety of combinatorial optimization problems.

A Noisy Chaotic Neural Network Approach to Topological Optimization of a Communication Network with Reliability Constraints

Network topological optimization in communication network is to find the topological layout of network links with the minimal cost under the constraint that all-terminal reliability of network is not less than a given level of system reliability. The all-terminal reliability is defined as the probability that every pair of nodes in the network can communicate with each other. The topological optimization problem is an NP-hard combinatorial problem. In this paper, a noisy chaotic neural network model is adopted to solve the all-terminal network design problem when considering cost and reliability. Two sets of problems are tested and the results show better performance compared to previous methods, especially when the network size is large.

Noisy Chaotic Neural Networks For Delay Constrained Multicast Routing

The QoS constrained multicast routing is studied widely these years due to the development of multimedia applications such as video-conferencing and video-on-demand. We apply noisy chaotic neural networks (NCNN) on the delay constrained multicast routing problem. The NCNN has richer and more flexible dynamics, and therefore is more efficient compared with the conventional Hopfield neural network as the latter is often trapped at local minima