System and method of global optimization using artificial neural networks
First Claim
1. A method of global optimization comprising the steps of:
- a. developing an approximate inverse model of a system using an artificial neural network (ANN), including the substepsconstructing a forward model having inputs and outputs with model parameters as inputs and behaviors as outputs,building a set of training examples consisting of a set of model parameters and their corresponding behaviors as derived from the forward model,developing an ANN with said set of training examples to build an inverse model, said inverse model having inputs and outputs with behaviors as inputs and model parameters as outputsstoring said inverse model;
b. determining the approximate model parameters of a system given a desired behavior of the system as inputs including the substepsretrieving said inverse model, computing the approximate model parameters from said inverse model; and
c. obtaining optimal model parameters for said system given the approximate model parameters including the substepsinitializing a local optimization process with the approximate model parameters,optimizing said approximate model parameters using the forward model and the optimization process resulting in the optimal model parameters.
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Abstract
A method of global optimization of complex, highly nonlinear, multivariant systems is described. An artificial neural network (ANN) is trained to create an approximate inverse model. The desired behavior for a particular system is then input to the inverse model to derive approximate model parameters for the particular system. Optimization of the approximate model parameters yields optimal model parameters. The method is applied to the synthesis of mechanical linkages where examples of a type of linkage mechanism are used to train an ANN and derive the approximate inverse model. Inverse models for a number of linkage mechanism types are derived and stored. For a linkage mechanism with unknown linkage parameters, a power spectrum representation of the coupler curve is developed and the inverse model for the type of linkage mechanism retrieved. The representation of the desired coupler curve is input and the approximate linkage parameters derived. Optimization further refines the linkage parameters.
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Citations
15 Claims
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1. A method of global optimization comprising the steps of:
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a. developing an approximate inverse model of a system using an artificial neural network (ANN), including the substeps constructing a forward model having inputs and outputs with model parameters as inputs and behaviors as outputs, building a set of training examples consisting of a set of model parameters and their corresponding behaviors as derived from the forward model, developing an ANN with said set of training examples to build an inverse model, said inverse model having inputs and outputs with behaviors as inputs and model parameters as outputs storing said inverse model; b. determining the approximate model parameters of a system given a desired behavior of the system as inputs including the substeps retrieving said inverse model, computing the approximate model parameters from said inverse model; and c. obtaining optimal model parameters for said system given the approximate model parameters including the substeps initializing a local optimization process with the approximate model parameters, optimizing said approximate model parameters using the forward model and the optimization process resulting in the optimal model parameters. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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