Automated neural network model selection algorithm for feedback linearization based control

2010 ◽  
Author(s):  
K. Vassiljeva ◽  
E. Petlenkov ◽  
J. Belikov
Keyword(s):  
Neural Network ◽  
Model Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Feedback Linearization ◽  
Selection Algorithm

A COMPARISON OF NEURAL NETWORK MODEL SELECTION STRATEGIES FOR THE PRICING OF S&P 500 STOCK INDEX OPTIONS

2007 ◽  
Vol 16 (06) ◽  
pp. 1093-1113 ◽  
Author(s):  
N. S. THOMAIDIS ◽  
V. S. TZASTOUDIS ◽  
G. D. DOUNIAS
Keyword(s):  
Neural Network ◽  
Model Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Information Criteria ◽  
Stock Index ◽  
Statistical Hypothesis ◽  
Neural Network Models ◽  
Index Options ◽  
Pruning Technique

This paper compares a number of neural network model selection approaches on the basis of pricing S&P 500 stock index options. For the choice of the optimal architecture of the neural network, we experiment with a “top-down” pruning technique as well as two “bottom-up” strategies that start with simple models and gradually complicate the architecture if data indicate so. We adopt methods that base model selection on statistical hypothesis testing and information criteria and we compare their performance to a simple heuristic pruning technique. In the first set of experiments, neural network models are employed to fit the entire options surface and in the second they are used as parts of a hybrid intelligence scheme that combines a neural network model with theoretical option-pricing hints.

ENSEMBLE OF NOVEL NEURAL NETWORK BASED ON CLONAL SELECTION ALGORITHM FOR SNEAK CIRCUIT ANALYSIS

2009 ◽  
Vol 18 (08) ◽  
pp. 1339-1351
Author(s):  
QI XINZHAN ◽  
LIU BINGJIE ◽  
JIA XINGLIANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Clonal Selection ◽  
Circuit Analysis ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
System Components ◽  
Functional Components ◽  
Sigmoid Functions

Neural network was introduced to sneak circuit analysis (SCA) in previous works. However, it may generate suspect results which were hard to explain. To overcome the shortcomings, this paper proposed a novel neural network model based on circuit architecture, named CArNN, which is used as an individual of an ensemble. In CArNN, neurons represented system components, and weights represented the joints between components. Models of neurons are sigmoid functions. Clone selection algorithm was used to train CArNNs population. The trained antibodies were used as individuals of an ensemble. The inputs of CArNN are states of switches, and the outputs are states of functional components. Ensemble predicted all possible functions of circuit. The sneak circuits can be discovered by comparing the predicted and designed functions. The results revealed that CArNNs can exactly discover sneak circuits.

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