Application of sequential learning neural networks to civil engineering modeling problems

Engineering with Computers

Volumn 18, Issue 2, 2002, Pages 138-147

  Rajasekaran, S ^a   Suresh, Deepa ^b   Vijayalakshmi Pai, G A ^a  

^a PSG COLLEGE OF TECHNOLOGY   (India)

^b Derby Design Consultants   (United Arab Emirates)

Author keywords

Gram Schmidt orthogonalization; High performance concrete; Minimization principle; Sequential learning neural network; Sigmoidal neuron; Steepest descent

Indexed keywords

CIVIL ENGINEERING; CONCRETE TESTING; ERROR ANALYSIS; FEEDFORWARD NEURAL NETWORKS; LEARNING ALGORITHMS; SEQUENTIAL MACHINES;

CIVIL ENGINEERING MODELING; GRAM-SCHMIDT ORTHOGONALIZATION; SEQUENTIAL LEARNING NEURAL NETWORKS; SIGMOIDAL NEURON; SINGLE HIDDEN LAYER NEURAL NETWORKS;

COMPUTER AIDED DESIGN;

EID: 0036361057     PISSN: 01770667     EISSN: None     Source Type: Journal    
DOI: 10.1007/s003660200012     Document Type: Article

References (8)

1. A sequential learning approach for single hidden layer neural networks
2. Multilayer neural networks: Approximated canonical decomposition of nonlinearity
3. A procedure for determining the topology of feed forward neural networks
4. Practical identification of NARMAX models using radial basis functions
5. Orthogonal least squares learning algorithm for radial basis function networks
6. Optimization of mix proportions for high performance concrete using cellular genetic algorithms