摘要:
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.
摘要:
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.
摘要:
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.
摘要:
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.
摘要:
Disclosed is a multi-layer neural network and circuit design method. The multi-layer neural network receiving an m-bit input and generating an n-bit output comprises a neuron having a cascaded pair of CMOS inverters and having an output node of the preceding CMOS inverter among the pair of CMOS inverters as its inverted output node and an output node of the succeeding CMOS inverter as its non-inverted output node, an input layer having m neurons to receive the m-bit input, an output layer having n neurons to generate the n-bit output, at least one hidden layer provided with n neurons to transfer the input received from the input layer to every upper hidden layer and the output layer, an input synapse group in a matrix having each predetermined weight value to connect each output of neurons on the input layer to each neuron of the output layer and at least one hidden layer, at least one transfer synapse group in a matrix having each predetermined weight value to connect each output of neurons of the hidden layer to each neuron of every upper hidden layer and the output layer, and a bias synapse group for biasing each input node of neurons of the hidden layers and the output layer.
摘要:
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.
摘要:
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.
摘要:
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.
摘要:
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.
摘要:
Disclosed is a multi-layer neural network and circuit design method. The multi-layer neural network receiving an m-bit input and generating an n-bit output comprises a neuron having a cascaded pair of CMOS inverters and having an output node of the preceding CMOS inverter among the pair of CMOS inverters as its inverted output node and an output node of the succeeding CMOS inverter as its non-inverted output node, an input layer having m neurons to receive the m-bit input, an output layer having n neurons to generate the n-bit output, at least one hidden layer provided with n neurons to transfer the input received from the input layer to the directly upper hidden layer or the output layer, an input synapse group in a matrix having each predetermined weight value to connect each output of neurons on the input layer to each neuron of the output layer and at least one hidden layer, at least one transfer synapse group in a matrix having each predetermined weight value to connect each output of neurons of the hidden layer to each neuron of its directly upper hidden layer or of the output layer, and a bias synapse group for biasing each input node of neurons of the hidden layers and the output layer.