Translation of a neural network into a rule-based expert system
First Claim
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1. A data processing system comprising:
- a neural network having an input layer of input units, and different layers of processing elements including an output layer of output processing elements and at least one hidden layer of hidden processing elements;
a translating means for translating knowledge in said neural network having an input layer of input units, and layers of processing elements including an output layer of output processing elements and at least one hidden layer of hidden processing elements, said translating means operable to translate knowledge in the outer layer and each hidden layer of the neural network into a corresponding layer set of rules, there being a corresponding layer set of rules for each of the outer layer and each hidden layer, and there being undefined hidden concepts embodied in the layer sets of rules;
a rewriting means for rewriting rules from the layer sets of rules by reformulating rules from one layer of said layers of processing elements in terms of rules of another layer of said layers of processing elements to eliminate undefined hidden concepts and thereby generate a rewritten set of rules, the rewritten set of rules comprising rules in IF-THEN form; and
a memory for receiving and storing the rewritten set of rules; and
wherein said translation means and rewriting means are operably connected to said neural network.
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Abstract
A rule-based expert system is generated from a neural network. The neural network is trained in such a way as to avoid redundancy and to select input weights to the various processing elements in such a way as to nullify the input weights which have smaller absolute values. The neural network is translated into a set of rules by a heuristic search technique. Additionally, the translation distinguishes between positive and negative attributes for efficiency and can adequately explore rule size exponential with a given parameter. Both explicit and implicit knowledge of adapted neural networks are decoded and represented as if--then rules.
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Citations
18 Claims
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1. A data processing system comprising:
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a neural network having an input layer of input units, and different layers of processing elements including an output layer of output processing elements and at least one hidden layer of hidden processing elements; a translating means for translating knowledge in said neural network having an input layer of input units, and layers of processing elements including an output layer of output processing elements and at least one hidden layer of hidden processing elements, said translating means operable to translate knowledge in the outer layer and each hidden layer of the neural network into a corresponding layer set of rules, there being a corresponding layer set of rules for each of the outer layer and each hidden layer, and there being undefined hidden concepts embodied in the layer sets of rules; a rewriting means for rewriting rules from the layer sets of rules by reformulating rules from one layer of said layers of processing elements in terms of rules of another layer of said layers of processing elements to eliminate undefined hidden concepts and thereby generate a rewritten set of rules, the rewritten set of rules comprising rules in IF-THEN form; and
a memory for receiving and storing the rewritten set of rules; and
wherein said translation means and rewriting means are operably connected to said neural network. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method of producing a rule-based expert system by use of a neural network having an input layer of input units and different layers of processing elements including an output layer and at least one hidden layer, the steps comprising:
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translating knowledge in the output layer and each hidden layer of the neural network into a corresponding layer set of rules, there being a corresponding layer set of rules for each of the outer layer and each hidden layer, and there being undefined hidden concepts embodied in the layer sets of rules; rewriting rules from the layer sets of rules by reformulating rules from one layer of said layers of processing elements in terms of rules of another layer of said layers of processing elements to eliminate undefined hidden concepts and thereby generate a rewritten set of rules, the rewritten set of rules comprising rules in IF-THEN form; and storing said rewritten rule set in a memory. - View Dependent Claims (13, 14, 15, 16, 17, 18)
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Specification