In this letter, the absolute exponential stability result of neural networks with asymmetric connection matrices is obtained, which generalizes the existing one about absolute stability of neural networks, by a new proof approach. It is demonstrated that the network time constant is inversely proportional to the global exponential convergence rate of the network trajectories to the unique equilibrium. A numerical simulation example is also given to illustrate the obtained analysis results.

This paper is concerned with bandwidth reservation for circuit groups which handle calls requesting asymmetric forward and backward multi-connections. A model of circuit group with sub-group configuration is treated, and two types of the bandwidth reservation schemes for the model are studied in this paper. One is a global scheme with monitoring the whole circuit group, and the other is a local scheme with monitoring each sub-group independently. The problems of optimizing the reservation parameters are formulated, and optimization methods for the problems are proposed. Numerical example are presented, and effectiveness of the reservation schemes with using the optimized parameters is numerically examined.

The recent progress of B-ISDN signaling systems has enabled networks to handle calls which require a wide variety of ATM connection sets. This paper is concerned with the circuit group which handles calls requesting asymmetric forward and backward multi-connections, and has the capability of both bandwidth negotiation and bandwidth reservation as a traffic control for enhancing call blocking performance. A model of the circuit group is first established focusing on the call level characteristics of the group, and then a method based on the reduced load approximation and an approximate analysis of a multirate group is proposed for calculating approximate blocking probabilities. The accuracy of the approximation method is evaluated numerically by comparing with an exact method and simulation. Further the impact of bandwidth negotiation and reservation on call blockings is examined based on numerical examples.

We propose an asymmetric neural network which can solve inequality-constrained combinatorial optimization problems that are difficult to solve using symmetric neural networks. In this article, a knapsack problem that is one of such the problem is solved using the proposed network. Additionally, we study condition for obtaining a valid solution. In computer simulations, we show that the condition is correct and that the proposed network produces better solutions than the simple greedy algorithm.