This paper presents efficient and easily implementable methods for the characteristic analysis and tolerance analysis of nonlinear resistive circuits using integer programming. In these methods, the problem of finding all characteristic curves or all solution sets (regions of possible operating points) is formulated as a mixed integer programming problem, and it is solved by a high-performance integer programming solver such as CPLEX. It is shown that the proposed methods can easily be implemented without making complicated programs, and that all characteristic curves or all solution sets are obtained by solving mixed integer programming problems several times. Numerical examples are given to confirm the effectiveness of the proposed methods.

A new algorithm is proposed for finding all solutions of piecewise-linear resistive circuits using separable programming. In this algorithm, the problem of finding all solutions is formulated as a separable programming problem, and it is solved by the modified simplex method using the restricted-basis entry rule. Since the modified simplex method finds one solution per application, the proposed algorithm can find all solutions efficiently. Numerical examples are given to confirm the effectiveness of the proposed algorithm.

Homotopy methods are known to be effective methods for finding DC operating points of nonlinear circuits with the theoretical guarantee of global convergence. There are several types of homotopy methods; as one of the most efficient methods for solving bipolar transistor circuits, the variable-gain homotopy (VGH) method is well-known. In this paper, we propose an efficient VGH method for solving bipolar and MOS transistor circuits. We also show that the proposed method converges to a stable operating point with high possibility from any initial point. The proposed method is not only globally convergent but also more efficient than the conventional VGH methods. Moreover, it can easily be implemented in SPICE.

In advance of network communication society by the internet, the way how to send data fast with a little loss becomes an important transportation problem. A generalized maximum flow algorithm gives the best solution for the transportation problem that which route is appropriated to exchange data. Therefore, the importance of the maximum flow algorithm is growing more and more. In this paper, we propose a Maximum-Flow Neural Network (MF-NN) in which branch nonlinearity has a saturation characteristic and by which the maximum flow problem can be solved with analog high-speed parallel processing. That is, the proposed neural network for the maximum flow problem can be realized by a nonlinear resistive circuit where each connection weight between nodal neurons has a sigmodal or piece-wise linear function. The parallel hardware of the MF-NN will be easily implemented.

This paper surveys the research topics and results on nonlinear theory and its applications which have been achieved in Japan or by Japanese researchers during the last decade. The paticular emphasis is placed on chaos, neural networks, nonlinear circuit analysis, nonlinear system theory, and numerical methods for solving nonlinear systems.