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Instead of the conventional static memory model based on an associative memory neural network, this paper proposes a dynamic memory model that is extended to the input-output system in order to allow interaction with external information. Based on the time correlation between the input signal and the network output, we analyze and investigate the network response to stored, nonstored, and mixed stored pattern signals, and the parameter dependencies inherent in the model. The possibility of a new mechanism, the chaotic dynamic model, which discriminates and identifies pattern signal information by combining the dynamic memory model with its extension is shown to be possible. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(5): 54–64, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10022 [ABSTRACT FROM AUTHOR]

We have been working toward the construction of a forward model arm employing a neural network model with electromyographic signals as the control input. We succeeded in estimating 1) the joint torque during isometric contraction in a plane from the electromyographic signals. 2) the path of motion from the degree of joint acceleration as well as 3) the path of motion from joint torque and 4) the position in three-dimensional space. In this paper, we present a new human interface employing a model of an arm, robot control of an artificial hand, and the learning of motion capability. In addition, based on the fact that we can estimate the position of the arm in three-dimensional space, we discuss as an example of the human interface the results of generating motion from electromyographic signals in a hypothetical arm using a neural network model. [ABSTRACT FROM AUTHOR]

Finding all solutions based on the piecewise-linear search is one of the steadiest methods to solve the non-linear equation. A feature of the method is that the local convergence problem is not involved. On the other hand, the number of region searches required even for a small-scale problem is tremendous. This paper proposes an efficient method for finding all solutions, restricting the problem to the analysis of bipolar circuits. Based on the range of values that each variable can take and the linearity of the equation, the solution candidate regions are restricted by evaluating the existence possibility of the solution for each of the multiple subset variables. The submatrix is considered and the size is varied, and the efficiency of the solution search is improved by testing a large number of linear regions at the same time. The proposed method works especially effectively when the signal propagation in the circuit has a directivity or the subcircuits have high independencies. Finally, the effectiveness of the proposed method is shown through actual computation examples. [ABSTRACT FROM AUTHOR]

Several methods have been presented recently for constructing the one-dimensional IIR digital filter by combining all-pass networks. It is known that the filter composed of the all-pass networks has a better computational efficiency and also low coefficient sensitivity due to the structural boundaries. In addition to the one-dimensional filters, a construction of the two-dimensional filter based on the all-pass network has also been proposed. A problem in that method, however, is the restriction on the realizable characteristics. This paper attempts to overcome that problem, and proposes a new construction of the two-dimensional digital filter using the all-pass network. The new approach employs the complex coefficient all-pass network instead of the conventional real-coefficient all-pass network. By this approach, the degree of freedom in the design is enhanced, and the characteristics which have been difficult or impossible to realize in the case of the real coefficient filters, are now made realizable. [ABSTRACT FROM AUTHOR]