Neural network that does not require repetitive training
AssigneeMotorola, Inc.
InventorShay-Ping T. Wang
Life of this patent
Estimating the term…
Abstract
A neural network, which may be implemented either in hardware or software, is constructed of neurons or neuron circuits each having only one significant processing element in the form of a multiplier. A hidden neuron in the neural network generates an output based on the product of a plurality of functions. The neural network utilizes a training algorithm which does not require repetitive training and which yields a global minimum to each given set of input vectors.
Claims9
A neural network having a plurality of network inputs and at least one network output, said neural network comprising:
- a plurality of input nodes, each input node coupled to a corresponding one of said plurality of network inputs and generating corresponding outputs;
- a plurality of hidden neurons, each hidden neuron having a plurality of neuron inputs and generating an output based on the product of a plurality of functions, wherein each of said plurality of functions is proportional to one of said plurality of neuron inputs raised to the power of one of a plurality of constants;
- means for distributing said output of each input node to one of said neuron inputs of at least one of said plurality of hidden neurons;
- and
- means for summing outputs of said hidden neurons and generating said at least one network output.
A neural network having a plurality of network inputs and a plurality of network outputs, said neural network comprising:
- an input node for receiving successive ones of said plurality of network inputs and generating corresponding node outputs;
- a plurality of hidden neurons, each hidden neuron having at least one neuron input and generating a neuron output based on a product of a plurality of functions, each of said plurality of functions being a function of said at least one neuron input;
- means for distributing said node outputs to said at least one neuron input of each of said plurality of hidden neurons;
- and
- means for summing neuron outputs and generating said plurality of network outputs, said means for summing including a plurality of summers each summing the outputs of predetermined ones of said hidden neurons and generating said network outputs.
Dependencies are inferred from the claim text.