Computational Intelligence in Economics and Finance: Volume II, 2. sējumsPaul P. Wang, Tzu-Wen Kuo Springer Science & Business Media, 2007. gada 11. jūl. - 228 lappuses Computational intelligence (CI), as an alternative to statistical and econometric approaches, has been applied to a wide range of economics and finance problems in recent years, for example to price forecasting and market efficiency. This book contains research ranging from applications in financial markets and business administration to various economics problems. Not only are empirical studies utilizing various CI algorithms presented, but so also are theoretical models based on computational methods. In addition to direct applications of computational intelligence, readers can also observe how these methods are combined with conventional analytical methods such as statistical and econometric models to yield preferred results. Chen, Wang, and Kuo have grouped the 12 contributions following their introductory chapter into applications of fuzzy logic, neural networks (including self-organizing maps and support vector machines), and evolutionary computation. All chapters were selected either by invitation or based on a careful selection and extension of best papers from the International Workshop on Computational Intelligence in Economics and Finance in 2005. Overall, the book offers researchers an excellent overview of current advances and applications of computational intelligence techniques to economics and finance problems. |
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1.–5. rezultāts no 42.
... output layer, via all hidden layers in between, as shown in Fig. 1. The reverse direction between any two layers is not allowed. This specific architecture makes the multilayer perceptron neural network unable to deal with the moving ...
... Output . . . . . . . . . . . . . . . . . . In using the multilayer perceptron neural network to represent (2), one needs to have an input layer with an infinite number of neurons (infinite memory of the past), namely, xt−1 ,x t−2 ...
... output for the output layer ˆxt. Layers are fully connected by weights: wij is the weight assigned to the ith input for the jth node in the hidden layer, whereas w j is the weight assigned to the jth node (in the hidden layer) for the ...
... Output Lag Lag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Context Layer Lag 4.2 Auto-associative Neural Networks While most economic and financial applications of the neural network consider its capability to develop ...
... Output Hidden Layer Hidden Layer Hidden Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fig. 3. The Auto-associative Neural Networks To introduce AANN and its relationship with principal components analysis ...
Saturs
1 | |
An Overview of Insurance Uses of Fuzzy Logic | 24 |
ArnoldF Shapiro 25 | 63 |
Estimating Female Labor Force Participation through Statistical | 93 |
An Application of Kohonens SOFM to the Management | 106 |
Trading Strategies Based on Kmeans Clustering and Regression Models | 123 |
Application of an Instance Based Learning Algorithm for Predicting | 144 |
Nonlinear GoalDirected CPPI Strategy | 183 |
A LogicalHeuristic Approach | 209 |
Index | 224 |
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Computational Intelligence in Economics and Finance: Volume II Paul P. Wang,Tzu-Wen Kuo Priekšskatījums nav pieejams - 2010 |
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