SYSTEM AND METHOD OF USING GENETIC PROGRAMMING AND NEURAL NETWORK TECHNOLOGIES TO ENHANCE SPECTRAL DATA
First Claim
1. A method of deriving a mapping transformation that transforms an input signal obtained from a subject under a first value of a parameter to an output signal obtainable from said subject under a second value of said parameter, comprising the steps of:
- a) creating a plurality of neural networks;
each of said neural network comprising a plurality of nodes arranged in neural layers being connected by a plurality of weighted synaptic links;
each said node further comprising a plurality of computational functions randomly selected from a plurality of functions in a plurality of function categories;
b) storing the configurations of said plurality of neural networks to a plurality of chromosomes;
said configurations recording the connections of said weighted synaptic links among nodes and said computational functions of each said nodes in at least one chromosome layer;
c) performing a first training on said plurality of neural networks by adjusting said weighted synaptic links to learn said mapping transformation using a data set;
said data set comprising a set of said input signals and a set of target signals;
said target signal obtained from said subject using a value of said parameter different from said input signal;
d) performing a second training on said plurality of neural networks by modifying said configurations of said plurality of neural networks, comprising the steps of;
i) applying genetic operators to said plurality of chromosomes, thus creating a second plurality of neural networks with different configurations;
ii) discarding neural networks in said second plurality of neural networks that do not satisfy at least one pre-defined constraint;
iii) repeating steps (i) and (ii) to replenish said discarded neural networks, andiv) replacing said plurality of neural networks by said second plurality of neural networks, ande) repeating steps (c) and (d) for a pre-determined number of generations such that in each said generation the configuration of each neural network may be altered and selected flexibly by said genetic operators to derive at an optimal neural network for said mapping transformation.
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Abstract
A signal transformation method that transforms an input signal obtained from a subject under a first value of a parameter to an output signal obtainable from said subject under a second value of said parameter is disclosed. The method creates a plurality of neural networks and subjects them to learn the mapping transformation. Genetic programming is used to evolve said plurality of neural networks by applying genetic operators to alter the configurations of said plurality of neural networks. The process of neural learning and genetic altering repeats until a predetermined number of generations is reach. The neural network that performs the mapping transformation best can be selected as the optimal neural network. This optimal neural network can be used subsequently to transform a second input signal to a second output signal for a pre-defined value of the parameter. The method of deriving the mapping transformation and the method of using the optimal neural network can be implemented as software applications that run on a data processing system.
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Citations
33 Claims
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1. A method of deriving a mapping transformation that transforms an input signal obtained from a subject under a first value of a parameter to an output signal obtainable from said subject under a second value of said parameter, comprising the steps of:
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a) creating a plurality of neural networks;
each of said neural network comprising a plurality of nodes arranged in neural layers being connected by a plurality of weighted synaptic links;
each said node further comprising a plurality of computational functions randomly selected from a plurality of functions in a plurality of function categories;b) storing the configurations of said plurality of neural networks to a plurality of chromosomes;
said configurations recording the connections of said weighted synaptic links among nodes and said computational functions of each said nodes in at least one chromosome layer;c) performing a first training on said plurality of neural networks by adjusting said weighted synaptic links to learn said mapping transformation using a data set;
said data set comprising a set of said input signals and a set of target signals;
said target signal obtained from said subject using a value of said parameter different from said input signal;d) performing a second training on said plurality of neural networks by modifying said configurations of said plurality of neural networks, comprising the steps of; i) applying genetic operators to said plurality of chromosomes, thus creating a second plurality of neural networks with different configurations; ii) discarding neural networks in said second plurality of neural networks that do not satisfy at least one pre-defined constraint; iii) repeating steps (i) and (ii) to replenish said discarded neural networks, and iv) replacing said plurality of neural networks by said second plurality of neural networks, and e) repeating steps (c) and (d) for a pre-determined number of generations such that in each said generation the configuration of each neural network may be altered and selected flexibly by said genetic operators to derive at an optimal neural network for said mapping transformation. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 14, 15, 16, 17)
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9. A method of producing a transformed output signal from a sampled input signal, said transformed output signal obtainable of a pre-selected subject under a pre-determined value of a parameter, said sampled input signal obtained of said pre-selected subject under a pre-selected value of said parameter, said method comprising the steps of:
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a) providing an optimal neural network derived using a method of deriving a mapping transformation that transforms an input signal obtained from a subject under a first value of a parameter to an output signal obtainable from said subject under a second value of said parameter, said method of deriving comprising the steps of; i) creating a plurality of neural networks;
each of said neural network comprising a plurality of nodes arranged in neural layers being connected by a plurality of weighted synaptic links;
each said node further comprising a plurality of computational functions randomly selected from a plurality of functions in a plurality of function categories;ii) storing the configurations of said plurality of neural networks to a plurality of chromosomes;
said configurations recording the connections of said weighted synaptic links among nodes and said computational functions of each said nodes in at least one chromosome layer;iii) performing a first training on said plurality of neural networks by adjusting said weighted synaptic links to learn said mapping transformation using a data set;
said data set comprising a set of said input signals and a set of target signals;
said target signal obtained from said subject using a value of said parameter different from said input signal;iv) performing a second training on said plurality of neural networks by modifying said configurations of said plurality of neural networks, comprising the steps of; (1) applying genetic operators to said plurality of chromosomes, thus creating a second plurality of neural networks with different configurations; (2) discarding neural networks in said second plurality of neural networks that do not satisfy at least one pre-defined constraint; (3) repeating steps (i) and (ii) to replenish said discarded neural networks, and (4) replacing said plurality of neural networks by said second plurality of neural networks, and v) repeating steps (c) and (d) for a pre-determined number of generations such that in each said generation the configuration of each neural network may be altered and selected flexibly by said genetic operators to derive at said optimal neural network for said mapping transformation; b) feeding said sampled input signal to said optimal neural network; c) entering said pre-determined value of said parameter to said optimal neural network, and d) performing said mapping transformation to produce said transformed output signal.
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10. A method for deriving a mapping transformation that transforms an input signal to a target signal comprising the steps of:
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a) collecting a data set, said data set further comprising a set of said input signals and a set of said target signals;
wherein each of said target signal indicating the desired output response of said mapping transformation for said input signal;b) creating a plurality of neural networks;
each of said neural network comprising a plurality of nodes arranged in neural layers being connected by a plurality of weighted synaptic links;c) randomly selecting computational functions for said nodes from a plurality of functions in a plurality of function categories; d) storing the configurations of said plurality of neural networks to a plurality of chromosomes;
said chromosomes further comprising at least one chromosome layer;e) training said plurality of neural networks to learn said mapping transformation by adjusting the weight values of said weighted synaptic links so that a fitness score can be optimized;
said fitness score measuring the mapping transformation performance of said neural network;f) modifying said configurations of said plurality of neural networks by repetitively performing the steps of; i) selecting at least one candidate chromosome from said plurality of chromosomes according to a pre-specified criteria; ii) generating at least one child chromosome by a genetic operator, and iii) applying at least one global constraint to said child chromosome and repeating steps (i) and (ii) if said child chromosome fails to satisfy said at least one constraint so that a plurality of child chromosomes can be generated;
said plurality of child chromosomes defining said configurations of said plurality of neural networks, andg) repeating steps (e) and (f) for a predetermined number of generations such that in each said generation the configuration of each neural network may be altered and selected flexibly by said genetic operator to derive at an optimal neural network for said mapping transformation. - View Dependent Claims (11, 12, 13, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31)
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32. A computer system for deriving a signal transformation that transforms an input signal obtained from a subject under a first value of a parameter to an output signal obtainable from said subject under a second value of said parameter, comprising:
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a) a data collection module configured to store a data set;
said data set further comprising a plurality of input signals and a plurality of target signals;b) a data processing module configured to prepare said data set for subsequent analysis; c) a neural network module configured to i) construct a plurality of neural networks;
each said neural network comprising a plurality of nodes interconnected by a plurality of weighted synaptic links;
the configurations of said neural networks being stored in a plurality of chromosomes, andii) train said plurality of neural networks to learn said signal transformation using said plurality of input signals and said plurality of target signals; d) a fitness evaluation module configured to evaluates the performances of said plurality of neural networks in performing said signal transformation; and
stores those neural networks having high performance to a Top-B database, ande) a genetic programming module configured to modify said configurations of said plurality of neural networks by repetitively performing the steps of i) selecting at least one candidate chromosome from said plurality of chromosomes according to a pre-specified criteria; ii) generating at least one child chromosome by a genetic operator, and iii) applying at least one global constraint to said child chromosome and repeating steps (i) and (ii) if said child chromosome fails to satisfy said at least one constraint so that by repetitively executing said genetic programming module, said neural network module and said fitness evaluation module, the performances of said plurality of neural networks improve and an optimal neural network configuration can be retrieved from said Top-B database that achieves the best performance in performing said signal transformation.
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33. An article of manufacture for signal enhancement of a signal processing apparatus comprising:
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a) a data handling module configured to accept an input signal and prepare said input signal for subsequent analysis, and b) a neural network processing module comprising at least one neural network, each said neural network optimally trained to transform an input signal of a subject to an output signal of said subject according to at least one pre-determined parameter value;
said at least one pre-determined parameter value inputting to at least one input node of said neural network.
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Specification