03950nam a2200361 a 4500 vtls000047732 KUKTEM 20251117141419.0 m d cr un ---uuuua 100804t2009 paua fsb 001 0 eng d 9781599048987 (electronic book) 9781599048970 201107131913 VLOAD 201104221149 Fida 201008101151 ida 201008050900 ida 201008041124 ida UMP HG106 .A78 2009 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 Original: xxiii, 517 p. : ill. ; 29 cm. Mode of access: Internet Finance Computer simulation Finance Mathematical models Finance Computer programs Neural networks (Computer science) Zhang, Ming http://find.galegroup.com.libraryumpsa.idm.oclc.org/openurl/openurl?url_ver=Z39.88-2004&url_ctx_fmt=info:ofi/fmt:kev:mtx:ctx&req_dat=info:sid/gale:ugnid:myump&res_id=info:sid/gale:GVRL&ctx_enc=info:ofi:enc:UTF-8&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft_id=info:sid/gale:bmcode:recid/2RAV Available for Universiti Malaysia Pahang via Gale Virtual Reference Library. Click here to access 0 0 0 1 10000 10000 2019-09-04 0 HG106 .A78 2009 0000049273 2019-09-04 00:00:00 1 2019-09-04 EBOOK VIRTUA40 16468 16474 VTLSSORT0060*0070*0080*0200*0201*0400*0900*2450*2600*4400*5040*5050*5200*5330*5340*5380*6500*6501*6502*6503*7000*8560*9991