Embedding monotonicity and concavity in the training of neural networks by means of genetic algorithms: Application to multiphase flow

Computers and Chemical Engineering

Volumn 28, Issue 9, 2004, Pages 1701-1713

  Tarca, Laurentiu A ^a   Grandjean, Bernard P A ^a   Larachi, Faïçal ^a  

^a UNIVERSITÉ LAVAL   (Canada)

Author keywords

AARE; absolute relative error; ARE; average absolute relative error; G; G+T; GA; gas, generalization; generalization and training; genetic algorithm; genetic hill climber; GHC; L; liquid; MCI; monotonicity concavity information; neural network; NN; S; solid; STDEV

Indexed keywords

CORRELATION METHODS; GENETIC ALGORITHMS; MULTIPHASE FLOW; OPTIMIZATION; PACKED BEDS; PROBLEM SOLVING;

CONCAVITY; MONOTONICITY;

NEURAL NETWORKS;

COMPUTER;

ARTICLE; ARTIFICIAL NEURAL NETWORK; EMBEDDING; FLOW; GAS; GENETIC ALGORITHM; INFORMATION; LIQUID; MODEL; MUTATION; PHENOMENOLOGY; REACTOR; WEIGHT;

EID: 2442528734     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2004.01.003     Document Type: Article

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