Artificial higher order neural networks for economics and business
Artificial higher order neural networks for economics and business [electronic resource] /
Ming Zhang, editor
- Hershey, Pa. : Information Science Reference, c2009
- Gale virtual reference library .
Includes bibliographical references and index
Artificial higher order neural network nonlinear models: SAS NLIN or HONNs? -- Higher order neural networks with Bayesian confidence measure for the prediction of the EUR/USD exchange rate -- Automatically identifying predictor variables for stock return prediction -- Higher order neural network architectures for agent-based computational economics and finance -- Foreign exchange rate forecasting using higher order flexible neural tree -- Higher order neural networks for stock index modeling -- Ultra high frequency trigonometric higher order neural networks for time series data analysis -- Artificial higher order pipeline recurrent neural networks for financial time series prediction -- A novel recurrent polynomial neural network for financial time series prediction -- Generalized correlation higher order neural networks for financial time series prediction -- Artificial higher order neural networks in time series prediction -- Application of pi-sigma neural networks and ridge polynomial neural networks to financial time series prediction -- Electric load demand and electricity prices forecasting using higher order neural networks trained by Kalman filtering -- Adaptive higher order neural network models and their applications in business -- CEO tenure and debt: an artificial higher order neural network approach -- Modelling and trading the soybean-oil crush spread with recurrent and higher order networks: a comparative analysis -- Fundamental theory of artificial higher order neural networks -- Dynamics in artificial higher order neural networks with delays -- A new topology for artificial higher order neural networks: polynomial kernel networks -- High speed optical higher order neural networks for discovering data trends and patterns in very large databases -- On complex artificial higher order neural networks: dealing with stochasticity, jumps and delays -- Trigonometric polynomial higher order neural network group models and weighted kernel models for financial data simulation and prediction.
Provides significant, informative advancements in the subject and introduces the concepts of HONN group models and adaptive HONNs
Electronic reproduction.
Farmington Hills, Mi :
Gale.
Available via World Wide Web
Mode of access: Internet
9781599048987 (electronic book) 9781599048970
Finance--Computer simulation
Finance--Mathematical models
Finance--Computer programs
Neural networks (Computer science)
Includes bibliographical references and index
Artificial higher order neural network nonlinear models: SAS NLIN or HONNs? -- Higher order neural networks with Bayesian confidence measure for the prediction of the EUR/USD exchange rate -- Automatically identifying predictor variables for stock return prediction -- Higher order neural network architectures for agent-based computational economics and finance -- Foreign exchange rate forecasting using higher order flexible neural tree -- Higher order neural networks for stock index modeling -- Ultra high frequency trigonometric higher order neural networks for time series data analysis -- Artificial higher order pipeline recurrent neural networks for financial time series prediction -- A novel recurrent polynomial neural network for financial time series prediction -- Generalized correlation higher order neural networks for financial time series prediction -- Artificial higher order neural networks in time series prediction -- Application of pi-sigma neural networks and ridge polynomial neural networks to financial time series prediction -- Electric load demand and electricity prices forecasting using higher order neural networks trained by Kalman filtering -- Adaptive higher order neural network models and their applications in business -- CEO tenure and debt: an artificial higher order neural network approach -- Modelling and trading the soybean-oil crush spread with recurrent and higher order networks: a comparative analysis -- Fundamental theory of artificial higher order neural networks -- Dynamics in artificial higher order neural networks with delays -- A new topology for artificial higher order neural networks: polynomial kernel networks -- High speed optical higher order neural networks for discovering data trends and patterns in very large databases -- On complex artificial higher order neural networks: dealing with stochasticity, jumps and delays -- Trigonometric polynomial higher order neural network group models and weighted kernel models for financial data simulation and prediction.
Provides significant, informative advancements in the subject and introduces the concepts of HONN group models and adaptive HONNs
Electronic reproduction.
Farmington Hills, Mi :
Gale.
Available via World Wide Web
Mode of access: Internet
9781599048987 (electronic book) 9781599048970
Finance--Computer simulation
Finance--Mathematical models
Finance--Computer programs
Neural networks (Computer science)