Abstract: A mapping circuit includes a linear circuit for outputting a signal which is linearly changed with respect to its input, a non-linear circuit for outputting a signal which is non-linearly changed with respect to its input, and an adder for summing the output signals of the linear and non-linear circuits and an external input signal. A chaotic neuron circuit using the mapping circuit has a simple structure and more precise chaos characteristics. A chaotic neural network can thus be formed by the serial and/or parallel interconnection of a plurality of chaotic neuron circuits, wherein the weight of each neuron is controlled.

Abstract: A chaotic recurrent neural network includes N chaotic neural networks for receiving an external input and the outputs of N-1 chaotic neural networks among said N chaotic neural networks and performing an operation according to the following dynamic equation ##EQU1## wherein W.sub.ij is a synapse connection coefficient of the feedback input from the "j"th neuron to the "i"th neuron, X.sub.i (t) is the output of the "i"th neuron at time t, and .gamma..sub.i, .alpha. and and k are a time-delaying constant, a non-negative parameter and a refractory time attenuation constant, respectively, and wherein Z.sub.i (t) represents X.sub.i (t) when i belongs to the neuron group I and represents a.sub.i (t) when i belongs to the external input group E. Also, a learning algorithm for the chaotic recurrent neural network increases its learning efficiency.

Abstract: A non-linear circuit includes a first variable resistor one end of which is applied with an input signal, an amplifier whose inverting input is connected to the other end of the first variable resistor and whose non-inverting input is connected to ground, a second variable resistor one end of which is connected to the inverting input of the amplifier, a third variable resistor one end of which is connected to the output of the amplifier and the other end being connected to the other end of the second variable resistor, and a fourth variable resistor one end of which is applied with the input signal and the other end being connected to the third variable resistor.

Abstract: A speech recognition system for recognizing the remote-controlling vocal commands of TV sets and VCRs comprises a microphone for receiving the speech pronounced by a user; a speech analyzer for analyzing the speech input via the microphone; circuitry for detecting a vocal section of the speech from the speech analyzer and performing a time-axis normalization and a binarization for the detected vocal section; and a multilayer neural network for receiving the binarization data from the aforementioned circuitry and then performing the learning, to thereby output the speech recognition result. Accordingly, the present invention can enhance the recognition ratio of speech.

Abstract: A self-learning multi layer neural network and the learning method thereof are characterized in that N-bit input data and M-bit desired output data are received, a weight value of each synapse is adjusted so as to produce output data corresponding to the input data, and self-learning is performed while proceeding to a next layer. Thus, it is not necessary for the user to input and adjust all the weight values of the respective synapse while the network performs self-learning and a desired function.

Abstract: A programmable multilayer neural network includes a weight storing circuit for storing the weight of each synapse to perform an intended function, an interfacing circuit for transmitting the weight value stored in the storing circuit to each synapse, and a multilayer neural network circuit programmed to have the weight from the weight storing circuit and for outputting an intended output.

Abstract: A synapse MOS transistor has gate electrodes of different lengths, different widths or different lengths and widths, between one source region and one drain region. Thus, when using the synapse MOS transistor to implement a neural network, the chip area can be greatly reduced.

Abstract: Disclosed is a multi-layer neural network and circuit design method.

Abstract: Disclosed is a multi-layer neural network and circuit design method.

Abstract: A priority encoder using a MOS array and neural network concepts is composed of an input side neuron group, an output side neuron group, a synapse group, a bias group and inverters. The encoder is simple in its construction and fast in its operating speed compared with the conventional priority encoders utilizing simple Boolean logic.

Abstract: A conversion circuit of binary dither image to multilevel image comprises a counter utilizing concepts of a neural network, an 8 bit register and 8 OR gates, resulting in high speed of operation. The counter uses a neural network based on the Hopfield model and is made up of an input synapse group, a first bias synapse group, a feedback synapse group, a second bias synapse group, a neuron group and an invertor group.

Abstract: An error correction circuit is provided which uses NMOS and PMOS synapses to form neural network type responses to a coded multi-bit input. Use of MOS technology logic in error correction circuits allows such devices to be easily interfaced with other like technology circuits without the need to use distinct interface logic as with conventional error correction circuitry.

Abstract: An error correction circuit is provided which uses NMOS and PMOS synapses to form network type responses to a coded multi-bit input. Use of MOS technology logic in error correction circuits allows such devices to be easily interfaced with other like technology circuits without the need to use distinct interface logic as with conventional error correction circuitry.

Abstract: A divider using neural network configurations comprises a subtractor, a selecting means, a first latch means, a second latch means, a shift register and a control means. The subtractor of the divider comprises plural inverters and plural 3-bit full-adders which are composed of four output lines, an input synapse group, a first bias synapse group, a second bias synapse group, a feedback synapse group, a neuron group and an inverter group.

Abstract: A divider circuit for efficiently and quickly performing a hardware implemented division by adopting a neural network architecture. The circuit includes a series of cascaded subtracter components that complement the divisor input and effectively perform an adder function. The subtracters include a synaptic configuration consisting of PMOS transistors, NMOS transistors, and CMOS inverters. The components are arranged in accordance with the predetermined connection strength assigned to each of the transistors and its respective position in the neural type network arrangement.

Abstract: A floating point adder circuit using neural network concepts and having high speed operation is obtained by a controlling circuit using a comparator and an operating circuit using an adder and a subtractor.