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.

In this paper, we discuss an emergent behavior of a multi-elevator system. The system includes multiple elevators in an office building and the Poisson arrival of passengers as its input. Elevators move up and down to serve calls and carry passengers according to given working rules. The system is a representative discrete event dynamic system, and is a nonlinear complex system. When people leave a building at the closing time, the down-peak traffic of passengers occurs. We show numerically that (1) this causes a jamming effect, which reduces the transportation efficiency, (2) there exists a threshold in the arrival rate of passengers, at which the traffic rate starts decreasing, and (3) this jamming effect is due to the synchronization of elevators. Then we propose a dispatching control that prevents elevators from synchronizing. This control is applied to each elevator as an anxiliary working rule. We can remove the jamming effect and recover the transportation efficiency by the control.

Methods of automatically adjusting delay time and feedback gain in controlling chaos by delayed feedback control are proposed. These methods are based on a gradient-descent procedure minimizing the squared error between the current state and the delayed state. The method of adjusting delay time and that of adjusting feedback gain are applied to controlling chaos in numerical calculations of Rossler Equation and Duffing equation, respectively. Both methods are confirmed to be successful.

A numerical method is presented for calculating transverse and non-transverse (or tangent) types of homoclinic points of a two-dimensional noninvertible map having an invariant set that reduces to a one-dimensional noninvertible map. To illustrate bifurcation diagrams of homoclinic points and transitions of chaotic states near the bifurcation parameter values, three systems including coupled chaotic maps are studied.

This paper proposes a piecewise linear non-autonomous chaos generator that includes a switched inductor with time delay. The dynamics can be grasped by using piecewise exact solutions and one-dimensional return map can be derived rigorously. Using the return map, we formulate bifurcation equations and clarify tangent bifurcation route to chaos. Rough global bifurcation sets are given. Some of chaotic attractors are verified in the laboratory.

A new current-mode sampled-data chaos circuit is proposed. The proposed circuit is composed of an operation block, a parameter block, and a delay block. The nonlinear mapping functions of this circuit are generated in the neuro-fuzzy based operation block. And these functions are determined by supervised learning. For the proposed circut, the dynamics of the learning and the state of the chaos are analyzed by computer simulations. The design conditions concerning the bifurcation diagram and the nonlinear mapping function are presented to clarify the chaos generating conditions and the effect of nonidealities of the proposed circuit. The simulation results showed that the nonlinear mapping functions can be realized with the precision of the order of several percent and that different kinds of bifurcation modes can be generated easily.

In order to construct fuzzy systems automatically, there are many studies on combining fuzzy inference with neural networks. In these studies, fuzzy models using self-organization and vector quantization have been proposed. It is well known that these models construct fuzzy inference rules effectively representing distribution of input data, and not affected by increment of input dimensions. In this paper, we propose a destructive fuzzy modeling using neural gas network and demonstrate the validity of a proposed method by performing some numerical examples.

This peper proposes a simplified model of the well-known two-neuron neural oscillator. By eliminating one of the two positive feedback synapses in the neural oscillator, learning for the in-phase control of the oscillator is shown to be achievable via a very simple learning rule. The learning rule is devised in such a way that only the plasticity of two synaptic weights are required. We demonstrate some examples of the synchronization learning to validate the efficiency of the learning rule, and finally by illustrating the dynamics of the synchronization learning and by using computer simulation, we show the convergence behavior and the stability of the learning rule for the two-neuron simple neural oscillator.

In this paper we develop a new family of on-line adaptive learning algorithms for blind separation of time delayed and convolved sources. The algorithms are derived for feedforward and fully connected feedback (recurrent) neural networks on basis of modified natural gradient approach. The proposed algorithms can be considered as generalization and extension of existing algorithms for instantaneous mixture of unknown source signals. Preliminary computer simulations confirm validity and high performance of the proposed algorithms.

Nonlinearity is an important factor in the biological neural networks. The motion perception and learning in them have been studied on the simplest type of nonlinearity, multiplication. In this paper, asymmetrical neural networks with nonlinear function, are studied in the biological neural networks. Then, the nonlinear higher-order system is discussed in the neural networks. The second-order system in the nonlinear biological system is shown to play an important role in the movement detection. From the theoretical analysis, it is shown that the third-order one does not contribute to the detection and the fourth-order one becomes to the second-order in the movement detection function. Hassenstein and Reichardt network (1956) and Barlow and Levick network (1965) of movements are similar to the asymmetrical network developed here. To make clear the difference among these asymmetrical networks, we derive α-equation of movement, which shows the detection of movement. During the movement, we also can derive the movement equation, which implies the movement direction regardless of the parameter α.

The dipole-dipole interaction among excitons is shown to give rise to an intrinsic nonlinearity, which yields a localized mode in a forbidden band, providing a coherent state for quantum computation. Employing this mode, a quantum XOR (exclusive OR) gate is proposed. A block structure of quantum dot arrays is also proposed, to implement quantum circuits comprising the quantum XOR gates for computation.

Microcellular radio direct-sequence code division multiple access (DC-CDMA) system using optical link to connect their base stations to a central station is a solution of cost-effective and efficient spectrum reuse to meet the growing demand for mobile communications. In addition to the inherent multiuser interference (MUI) of CDMA signals, the system capacity is significantly reduced by a nonlinear distortion (NLD) due to the nonlinearity of optical link. In this paper, a two-stage cancellation technique is introduced into the system to cancel both the MUI and the NLD. It is performed at the receiver of the central station where the random ingredients of all user signals are estimated, and the MUI and the NLD are rebuilt and removed from the received signal. The validity of the cancellation technique is theoretically analyzed and shown by the numerical results. The analytical method and its results are also applicable to other general nonlinear CDMA.

A neural network of massively interconnected digital neurons is presented for the total coloring problem in this paper. Given a graph G (V, E), the goal of this NP-complete problem is to find a color assignment on the vertices in V and the edges in E with the minimum number of colors such that no adjacent or incident pair of elements in V and E receives the same color. A graph coloring is a basic combinatorial optimization problem for a variety of practical applications. The neural network consists of (N+M) L neurons for the N-vertex-M-edge-L-color problem. Using digital neurons of binary outputs and range-limited non-negative integer inputs with a set of integer parameters, our digital neural network is greatly suitable for the implementation on digital circuits. The performance is evaluated through simulations in random graphs with the lower bounds on the number of colors. With a help of heuristic methods, the digital neural network of up to 530, 656 neurons always finds a solution in the NP-complete problem within a constant number of iteration steps on the synchronous parallel computation.

In fully connected Multilayer perceptron (MLP), all the hidden units are activated by samples from the whole input space. For complex problems, due to interference and cross coupling of hidden units' activations, the network needs many hidden units to represent the problem and the error surface becomes highly non-linear. Searching for the minimum is then complex and computationally expensive, and simple gradient descent algorithms usually fail. We propose a network, where the input space is partitioned into local sub-regions. Subsequently, a number of smaller networks are simultaneously trained by overlapping subsets of the input samples. Remarkable improvement of training efficiency as well as generalization performance of this combined network are observed through various simulations.

For mobile communication systems with code division multiple access (CDMA), a new modulation scheme, π/2-shift BPSK, is proposed. The performance has been evaluated in terms of relative out-of-band power, bit-error rate (BER), and spectral efficiency. As the result, it is shown that the proposed scheme has an advantage over conventional BPSK, conventional QPSK, and π/4-shift QPSK under nonlinear amplification.

It is well known that offset errors in the multipliers of neural LSIs can have fatal effects on performance. The aim of this study is to understand theoretically how offset errors affect performance of neural LSIs. We have used a single-layer perceptron as an example, and compare our theoretically derived results with computer simulations. We have found that offset errors in the multipliers for the forward process can be canceled out through learning, but those for the updating process cannot be. We have examined the asymptotic behavior of learning for the updating process and derived a mathematical expression for dL, the excess of the averaged loss function L. The derived expression gives us a basis for estimating robustness with respect to the offset errors. Our analysis indicates that dL can be expressed in the form of a quadratic form of offset errors and the inverse of the Hessian matrix of L. We have found that increasing the number of synapses degrades the performacne. We have also learned that enlarging the input signal level and reducing the signal level of the desired response can be effective techniques for reducing the effects of offset errors of the updating process.

This paper presents a new multi-clustering network for the purpose of intelligent data classification. In this network, the first layer is a self-organized clustering layer and the second layer is a restricted clustering layer with a neighborhood mechanism. A new clustering algorithm is developed in this system for the efficiently use of parallel processors. This parallel algorithm enables the nodes of this network to be independently processed in order to minimize data communication load among processors. Using the parallel processors, the quite low calculation cost can be realized among the conventional networks. For example, a 4-processor parallel computing system has shown its ability to reduce the time taken for data classification to 26.75% of a single processor system without declining its performance.

Recently, the spatio-temporal filter using linear analog Cellular Neural Network (CNN), called CNN filter array, has been proposed for the purpose of dynamic image processing. In this paper, we propose a design method of descrete-time cellular neural network filter which selectively extracts the particular moving object from other moving objects and noise. The CNN filter array forms a spatio-temporal filter by arranging cells with a same function. Each of these cells is a simple linear analog temporal filter whose input is the weighted sum of its neighborhood inputs and outputs and each cell corresponds to each pixel. The CNN filter is formed by new model of discrete time CNN, and the filter parameters are determined by applying backpropagation algorithm in place of the analytic method. Since the number of connections between neurons in the CNN-type filter is relatively few, the required computation in the learning phase is reasonable amount. Further, the output S/N ratio is improved by introducing nonlinear element. That is, if the ratio of output to imput is smaller than a certain value, the output signal is treated as a noise signal and ought to be rejected. Through some examples, it is shown that the target object is enhanced in the noisy environment.

The pattern spectrum has been proposed to represent morphological size distribution of an image. However, the conventional pattern spectrum cannot extract approximate shape information from image objects spotted by noisy pixels since this is based only on opening. In this paper, a novel definition of the pattern spectrum, morphological multiresolution pattern spectrum (MPS), involving both opening and closing is proposed. MPS is capable of distinguishing details from approximate information of the image.

Based on a new search strategy using circuit simulation and simulated annealing with local search, a design tool is proposed to automate design or tuning process for CMOS operational amplifiers. A special-purpose circuit simulator and some heuristics are used to accomplish the design within reasonable time. For arbitrary circuit topology and specifications, the discrete optimization of cost function is performed by global and local search. Through the comparision of design results and the design of a low-power high-speed CMOS operational amplifier usable in 10-b 25-MHz pipelined A/D converters, it has been demonstrated that this tool can be used for designing high-performance operational amplifiers with less design knowledge and effort.

This paper presents an event-driven approach to the timing verification of latch-synchronized systems. The proposed method performs critical path extraction and timing error detection at the same time, and extracts the critical path only if necessary. By doing so, the complexity of analysis is reduced and efficiency is greatly improved over the conventional approaches which detect timing errors after extracting the complete critical paths of the system. Experimental results show that, compared to the existing methods, it provides a more than 12-fold improvement in speed on the average for ISCAS benchmark circuits, and the relative efficiency of analysis improves as the circuit size grows.

In this paper, we derive the mean message waiting times in a local area network that uses the Demand-Priority Access Method. We model the system as a two-priority M/G/1 queue with switchover time between service periods. This switchover time accounts for the polling and port selection performed by the repeater after each message transmission. The service discipline is non-preemptive and the length of the switchover time is dependent upon the priority class of the preceding message served as well as that of the message to be served next. The dependency in the switchover times is motivated by the polling and port selection operation of the protocol and it makes the analysis much more involved. In order to avoid the complexities of an exact analysis, we make some independence assumptions and thus obtain an approximate solution. Laplace-Stieltjes transforms of the stationary probability distribution functions for the waiting time of high- and normal-priority messages are derived, and subsequently, the expressions for the mean message waiting times. Numerical results computed using these expressions are verified using simulations which model the actual protocol. These numerical results which are shown to be accurate can be easily computed with widely available mathematical software.

In this paper we suggest the "goodness" of models using the imformation criterion AIC. The information criterion AIC is a statistic to estimate the badness of models. When we usually make the fuzzy rules, we aim to minimize inference error and the number of rules. But these conditions are the criteria to acquire an optimum rule-model by using the training data. In the general case of fuzzy reasoning, we aim to minimize the inference error for not only given training data, but also unknown data. So we have introduced a new information criterion based on AIC into the appraised criterion for estimating the acquired fuzzy rules. Experimental results are given to show the validity of using AIC.

A digital neural network approach is presented for the multilayer channel routing problem with the objective of crosstalk minimization in this paper. As VLSI fabrication technology advances, the reduction of crosstalk between interconnection wires on a chip has gained important consideration in VLSI design, because of the closer interwire spacing and the circuit operation at higher frequencies. Our neural network is composed of N M L digital neurons with one-bit output and seven-bit input for the N-net-M-track-2L-layer problem using a set of integer parameters, which is greatly suitable for the implementaion on digital technology. The digital neural network directly seeks a routing solution of satisfying the routing constraint and the crosstalk constraint simultaneously. The heuristic methods are effectively introduced to improve the convergence property. The performance is evaluated through solving 10 benchmark problems including Deutsch difficult example in 2-10 layers. Among the existing neural networks, the digital neural network first achieves the lower bound solution in terms of the number of tracks in any instance. Through extensive simulation runs, it provides the best maximum crosstalks of nets for valid routing solutions of the benchmark problems in multilayer channels.

In this paper, we present an error concealment method to recover damaged blocks for block-based image coding schemes. Imperfect transmission of image data results in damaged blocks in the reconstructed images. Hence recovering damaged image blocks is needed for reliable image communications. To recover damaged blocks is to estimate damaged blocks from the correctly received or undamaged neighborhood information with a priori knowledge about natural images. The recovery problem considered in our method is to estimate a larger block, which consists of a damaged block and the undamaged neighborhood, from the undamaged neighborhood. To find an accurate estimate, a set of the feature vectors is introduced and an estimate is expressed as a linear combination of the feature vectors. The proposed method recoveres damaged blocks by projecting the undamaged neighborhood information onto the feature vectors. The sequential projections onto the feature vectors algorithm is proposed to find the projection coefficients of the feature vectors to minimize the squared difference of an estimate and the undamaged neighborhood information. We tested our algorithm through computer simulations. The experimental results showed the proposed method ourperforms the frequency domain prediction method in the PSNR values by 4.0-5.0dB. Tthe reconstructed images by the proposed method provide a good subjective quality as well as an objective one.

In this paper, implementation of a first-order active complex filter with variable parameter using operational transconductance amplifiers (OTAs) and grounded copacitors is presented. The proposed configurations can be used as s key building block to realize high-order active complex filters with variable parameter in cascade and leapfrog configuration. Experimental results which are in good agreement with theoretical responses are also given o demonstrate the feasibility of the proposed configurations.

This letter describes a new computational method to obtain the bifurcation parameter value of a limit cycle in nonlinear autonomous systems. The method can calculate a parameter value at which local bifurcations; tangent, period-doubling and Neimark-Sacker bifurcations are occurred by using properties of the characteristic equation for a fixed point of the Poincare mapping. Conventionally a period of the limit cycle is not used explicitly since the Poincare mapping needs only whether the orbit reaches a cross-section or not. In our method, the period is treated as an independent variable for Newton's method, so an accurate location of the fixed point, its period and the bifurcation parameter value can be calculated simultaneously. Although the number of variables increases, the Jacobian matrix becomes simple and the recurrence procedure converges rapidly compared with conventional methods.

The present study investigated the human ability to selectively process pictures and words in free recall. We explored whether successful bias towards a subset of priority items occurs at the expense of the remaining items-i.e., whether successful priority item bias necessitates the dumping of information related to non-priority items. It has been shown that an increase in the percentage of correct recalls to items given priority in the pre-test instructions induces a decrease in the percentage of correct recalls for non-priority items. Even in a free recall experimental paradigm, the information dumping phenomenon was observed. However, there were no effects of stimulus presentation time and stimulus modality (picture vs. word) on the percentage of correct recalls detected.