This paper gives a new sufficient condition for cellular neural networks with delay (DCNNs) to be completely stable. The result is a generalization of two existing stability conditions for DCNNs, and also contains a complete stability condition for standard CNNs as a special case. Our new sufficient condition does not require the uniqueness of equilibrium point of DCNNs and is independent of the length of delay.

To improve speech coding quality, in particular, the long-term dependency prediction characteristics, we propose a new nonlinear predictor, i. e. , a fully connected recurrent neural network (FCRNN) where the hidden units have feedbacks not only from themselves but also from the output unit. The comparison of the capabilities of the FCRNN with conventional predictors shows that the former has less prediction error than the latter. We apply this FCRNN instead of the previously proposed recurrent neural networks in the code-excited predictive speech coding system (i. e. , CELP) and shows that our system (FCRNN) requires less bit rate/frame and improves the performance for speech coding.

In this paper we give the necessary and sufficient conditions for 2-dimensional discrete-time systems described by the signum function to be stable.

We consider a circuit composed of linear capacitors, nonlinear resistors, and dc voltage sources and show the possibility that the total energy dissipated at resistors in the above circuit is smaller than the energy stored at capacitors. Linear passive circuits cannot possess such a property.

This paper investigates the behavior of one-dimensional discrete-time binary cellular neural networks with both the A- and B-templates and gives the necessary and sufficient conditions for the above network to be stable for unspecified fixed boundaries.

This paper deals with the uniqueness of a solution of the basic equation obtained from the analysis of resistive circuits including ideal diodes. The equation in consideration is of the type of (A-)X=b, where A is a constant matrix, b a constant vector, X an unknown vector satisfying X 0, and a diagonal matrix whose diagonal elements take the value 0 or 1 arbitrarily. The necessary and sufficient conditions for the equation to have a unique solution X 0 for an arbitrary vector b are shown. Some numerical examples are given for the illustration of the result.

This paper deals with the number of solutions of circuit equations of nonlinear resistive circuits composed of linear and nonlinear one-port resistors, dc sources and linear active elements. By referring to the v-i characteristics of real diodes we assume that the second derivative as well as the first derivative of the v-i characteristics of nonlinear resistors is positive. The necessary and sufficient conditions are given for the equation to have a finite number (2n) of solutions and further the extension of this result is discussed.

We give the necessary and sufficient conditions for a one-dimensional discrete-time autonomous binary cellular neural networks to be stable in the case of fixed boundary. The results are complete generalization of our previous one [16] in which the symmetrical connections were assumed. The conditions are compared with some stability conditions so far known.

Analog Hopfield neural networks (HNNs) have so far been used to solve many kinds of optimization problems, in particular, combinatorial problems such as the TSP, which can be described by an objective function and some equality constraints. When we solve a minimization problem with equality constraints by using HNNs, however, the constraints are satisfied only approximately. In this paper we propose a circuit which rigorously realizes the equality constraints and whose energy function corresponds to the prescribed objective function. We use the SPICE program to solve circuit equations corresponding to the above circuits. The proposed method is applied to several kinds of optimization problems and the results are very satisfactory.

This paper surveyed the research topics and results on nonlinear circuits and systems which have been achieved in Japan or by Japanese researchers (sometimes as co-authors) during the last 20 years. The particular emphasis is placed on the analysis of nonlinear resistive circuits and periodic dynamic circuits.

This paper gives another canonical section of degree four for reactance one-port synthesis.

This paper gives two kinds of functions for which Uesaka's Conjecture, stating that the globally optimum (not a local minimum) of a quadratic function F(x)=-(1/2)xtAx in the n-dimensional hypercube may be obtained by solving a differential equation, holds true, where n denotes the dimension of the vector x. Uesaka stated in his paper that he proved the conjecture only for n=2. This corresponds to a very special case of this paper. The results of this paper suggest that the conjecture really holds for a wide class of quadratic functions and therefore support the conjecture partially.

We give necessary and sufficient conditions for a 1-D DBCNN (1-dimensional discrete-time binary cellular neural network) with an external input to be stable in terms of connection coefficients. The results are generalization of our previous one [18],[19] in which the input was assumed to be zero.

In this paper we study on the stability of an operating points of a nonlinear resistive circuits including transistors. A set of sufficient conditions for the operating point to be unstable are proposed. These conditions are a generalization of the well-known negative difference resistance (NDR) criteria.

The author once defined the Ω-matrix and showed that it played an important role for estimating the number of solutions of a resistive circuit containing active elements such as CCCS's. The Ω-matlix is a generalization of the wellknown P-matrix. This paper gives the necessary and sufficient conditions for the Ω-matrix.

We show some results concerning the number of solutions of the equation y+Ax=b (yTx=0, y0, x0) which plays a central role in the dc analysis of transistor circuits. In particular, we give sufficient conditions for the equation to possess exactly 2l (ln) solutions, where n is the dimension of the vector x.

The number of solutions of a nonlinear equation x = sgn(Wx) is discussed. The equation is derived for the determination of equilibrium points of a kind of Hopfield neural networks. We impose some conditions on W. The conditions correspond to the case where a Hopfield neural network has n neurons arranged on a ring, each neuron has connections only from k preceding neurons and the magnitude of k connections decrease as the distance between two neurons increases. We show that the maximum number of solutions for the above case is extremely few and is independent of the number of neurons, n, if k is less than or equal to 4. We also show that the number of solutions generally increases exponentially with n by considering the case where k = n-1.

This paper shows the necessary and sufficient condition for an admittance matrix to be realized as a network composed of three reactance two-ports and two resistors connected in cascade.