標(biāo)題: Titlebook: Economic Modeling Using Artificial Intelligence Methods; Tshilidzi Marwala Book 2013 Springer-Verlag London 2013 Artificial Intelligence.B [打印本頁] 作者: 戰(zhàn)神 時(shí)間: 2025-3-21 18:21
書目名稱Economic Modeling Using Artificial Intelligence Methods影響因子(影響力)
書目名稱Economic Modeling Using Artificial Intelligence Methods影響因子(影響力)學(xué)科排名
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書目名稱Economic Modeling Using Artificial Intelligence Methods網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Economic Modeling Using Artificial Intelligence Methods被引頻次
書目名稱Economic Modeling Using Artificial Intelligence Methods被引頻次學(xué)科排名
書目名稱Economic Modeling Using Artificial Intelligence Methods年度引用
書目名稱Economic Modeling Using Artificial Intelligence Methods年度引用學(xué)科排名
書目名稱Economic Modeling Using Artificial Intelligence Methods讀者反饋
書目名稱Economic Modeling Using Artificial Intelligence Methods讀者反饋學(xué)科排名
作者: 傳染 時(shí)間: 2025-3-21 21:33 作者: 取回 時(shí)間: 2025-3-22 03:28
1610-3947 niques are the time domain methods, time-frequency domain methods and fractals dimension approaches. Interesting economic problems such as causality versus correlation, simulating the stock market, modeling and978-1-4471-5919-3978-1-4471-5010-7Series ISSN 1610-3947 Series E-ISSN 2197-8441 作者: 鬼魂 時(shí)間: 2025-3-22 05:09
Book 2013onally, economic modeling has been modeled in the linear domain where the principles of superposition are valid. The application of artificial intelligence for economic modeling allows for a flexible multi-order non-linear modeling. In addition, game theory has largely been applied in economic model作者: jealousy 時(shí)間: 2025-3-22 12:40 作者: Mammal 時(shí)間: 2025-3-22 15:22
Missing Data Approaches to Economic Modeling: Optimization Approach,d using a multi-layered perceptron network, while the optimization techniques which are implemented are particle swarm optimization, genetic algorithms and simulated annealing. The results obtained are then compared.作者: Mammal 時(shí)間: 2025-3-22 17:17 作者: conspicuous 時(shí)間: 2025-3-22 22:39 作者: 滑動(dòng) 時(shí)間: 2025-3-23 02:00 作者: Enrage 時(shí)間: 2025-3-23 06:19
1610-3947 founded on artificial intelligence techniques.Addresses cau.Economic Modeling Using Artificial Intelligence Methods. .examines the application of artificial intelligence methods to model economic data. Traditionally, economic modeling has been modeled in the linear domain where the principles of su作者: 人充滿活力 時(shí)間: 2025-3-23 13:29
,Ernst Julius H?hnel Beethoven-Denkmal 1845,ercised at any time during the period and are, therefore, more complex due to the second random process they introduce. Support vector machines and multi-layered perceptron techniques are implemented using Bayesian technique to model American options and the results are compared.作者: 肉身 時(shí)間: 2025-3-23 14:54
Jason P. Harmon,Erin Stephens,John Loseyd using a multi-layered perceptron network, while the optimization techniques which are implemented are particle swarm optimization, genetic algorithms and simulated annealing. The results obtained are then compared.作者: 不易燃 時(shí)間: 2025-3-23 21:59
https://doi.org/10.1007/978-3-662-11229-8sing economic data estimation. The algorithm is used on data that contain ten economic variables. The results of the missing data imputation approach are compared to those from a feed-forward neural network.作者: 擴(kuò)大 時(shí)間: 2025-3-23 22:38
https://doi.org/10.1007/978-3-319-98437-7l inflation through the manipulation of interest rates. Given the historical inflation rate data, a control scheme is used to determine the interest rate that is required to attain the given inflation rate. The calculated interest rate is then compared to the historical inflation rate to evaluate the effectiveness of the control strategy.作者: CRASS 時(shí)間: 2025-3-24 03:38
Before Architecture. Vor der Architekturigated and compared, as well as optimizing the non-targeted variable to create efficient portfolios. The findings showed that GA is, indeed, a viable tool for optimizing a targeted portfolio using the presented fitness function.作者: Self-Help-Group 時(shí)間: 2025-3-24 09:20
Evolutionary Approaches to Computational Economics: Application to Portfolio Optimization,igated and compared, as well as optimizing the non-targeted variable to create efficient portfolios. The findings showed that GA is, indeed, a viable tool for optimizing a targeted portfolio using the presented fitness function.作者: 沙發(fā) 時(shí)間: 2025-3-24 14:05
Rough Sets Approach to Economic Modeling: Unlocking Knowledge in Financial Data,rket forecasting model an interesting problem. In this chapter a rough set theory based forecasting model is applied to the financial markets to identify a set of reducts and possibly a set of trading rules based on trading data.作者: 明確 時(shí)間: 2025-3-24 17:15
Introduction to Economic Modeling,ge discovery, including data mining and causality versus correlation. It also outlines some of the common errors in economic modeling with regard to data handling, modeling, and data interpretation. It surveys the relevant econometric methods and motivates for the use of artificial intelligence meth作者: glacial 時(shí)間: 2025-3-24 20:36
Automatic Relevance Determination in Economic Modeling,bed in detail, relevant literature reviews are conducted, and their use is justified. The automatic relevance determination technique is then applied to determine the relevance of economic variables that are essential for driving the consumer price index. Conclusions are drawn and are explained with作者: 創(chuàng)作 時(shí)間: 2025-3-25 00:43 作者: ironic 時(shí)間: 2025-3-25 06:57
Bayesian Support Vector Machines for Economic Modeling: Application to Option Pricing, European styled options can be priced using the Black-Scholes equation and are only exercised at the end of the period but American options can be exercised at any time during the period and are, therefore, more complex due to the second random process they introduce. Support vector machines and mu作者: Volatile-Oils 時(shí)間: 2025-3-25 11:16 作者: 豐滿中國 時(shí)間: 2025-3-25 12:22 作者: BYRE 時(shí)間: 2025-3-25 19:30 作者: 徹底明白 時(shí)間: 2025-3-25 22:36
Evolutionary Approaches to Computational Economics: Application to Portfolio Optimization,rn characteristics. The portfolio is comprised of a number of arbitrarily performing trading strategies, plus a risk-free strategy in order to rebalance in a way similar to the traditional Capital Asset Pricing Model (CAPM) method of rebalancing portfolios. A format is presented for the design of a 作者: 積云 時(shí)間: 2025-3-26 01:38
Real-Time Approaches to Computational Economics: Self Adaptive Economic Systems, approach is implemented using a multi-layer perceptron as a weak-learner, where this weak-learner is improved by making use of the Learn++ algorithm. In addition, the Learn++ algorithm introduces the concept of on-line incremental learning, which means that the proposed framework is able to adapt t作者: 疲憊的老馬 時(shí)間: 2025-3-26 04:54 作者: arsenal 時(shí)間: 2025-3-26 12:17
Control Approaches to Economic Modeling: Application to Inflation Targeting,output model is constructed using a multi-layered perceptron network and a closed loop control strategy is adopted using a genetic algorithm to control inflation through the manipulation of interest rates. Given the historical inflation rate data, a control scheme is used to determine the interest r作者: POLYP 時(shí)間: 2025-3-26 14:56 作者: BILK 時(shí)間: 2025-3-26 20:26 作者: 后退 時(shí)間: 2025-3-26 23:06 作者: 演繹 時(shí)間: 2025-3-27 04:06 作者: 迎合 時(shí)間: 2025-3-27 08:50
Robin F. A. Moritz,Edward E. Southwickbed in detail, relevant literature reviews are conducted, and their use is justified. The automatic relevance determination technique is then applied to determine the relevance of economic variables that are essential for driving the consumer price index. Conclusions are drawn and are explained within the context of economic sciences.作者: 使絕緣 時(shí)間: 2025-3-27 10:44
Beethoven im Gespr?ch: Die neun Sinfonienapplied to model inflation by training these networks using the maximum-likelihood method. The results indicated that the SVM gives the best results followed by the MLP and then the RBF. The SVM with an exponential RBF gave the best results.作者: Dedication 時(shí)間: 2025-3-27 14:12
https://doi.org/10.1007/978-1-349-18856-7 approach is implemented using a multi-layer perceptron as a weak-learner, where this weak-learner is improved by making use of the Learn++ algorithm. In addition, the Learn++ algorithm introduces the concept of on-line incremental learning, which means that the proposed framework is able to adapt to new data.作者: Encephalitis 時(shí)間: 2025-3-27 19:21 作者: 的’ 時(shí)間: 2025-3-27 23:05 作者: Invertebrate 時(shí)間: 2025-3-28 04:07
,Costs of Nuclear Power—The Achilles’ Heel,for analyzing economic data. It also demonstrates that the accuracy of the artificial intelligence method depends on the problem at hand and that there is a wide scope of applying other emerging artificial intelligence techniques to model economic data.作者: Spinal-Fusion 時(shí)間: 2025-3-28 06:21
Peter A. Ricketti,Richard F. Lockeyge discovery, including data mining and causality versus correlation. It also outlines some of the common errors in economic modeling with regard to data handling, modeling, and data interpretation. It surveys the relevant econometric methods and motivates for the use of artificial intelligence meth作者: Organonitrile 時(shí)間: 2025-3-28 13:33
Robin F. A. Moritz,Edward E. Southwickbed in detail, relevant literature reviews are conducted, and their use is justified. The automatic relevance determination technique is then applied to determine the relevance of economic variables that are essential for driving the consumer price index. Conclusions are drawn and are explained with作者: 單色 時(shí)間: 2025-3-28 17:49
Beethoven im Gespr?ch: Die neun Sinfonienapplied to model inflation by training these networks using the maximum-likelihood method. The results indicated that the SVM gives the best results followed by the MLP and then the RBF. The SVM with an exponential RBF gave the best results.作者: 過份 時(shí)間: 2025-3-28 20:43 作者: hedonic 時(shí)間: 2025-3-29 02:32
https://doi.org/10.1007/978-3-476-03589-9ncial markets are the foundation of every economy and there are many aspects that affect the direction, volume, price, and flow of traded stocks. The markets’ weakness to external and non-financial features as well as the ensuing volatility makes the development of a robust and accurate financial ma作者: bibliophile 時(shí)間: 2025-3-29 05:30 作者: 解決 時(shí)間: 2025-3-29 08:36
https://doi.org/10.1007/978-3-662-11229-8ause and effect exercise, that is, feed-forward network. An auto-associative neural network is combined with genetic algorithm and then applied to missing economic data estimation. The algorithm is used on data that contain ten economic variables. The results of the missing data imputation approach 作者: BOOR 時(shí)間: 2025-3-29 12:41 作者: Assemble 時(shí)間: 2025-3-29 16:33
https://doi.org/10.1007/978-1-349-18856-7 approach is implemented using a multi-layer perceptron as a weak-learner, where this weak-learner is improved by making use of the Learn++ algorithm. In addition, the Learn++ algorithm introduces the concept of on-line incremental learning, which means that the proposed framework is able to adapt t作者: 尖牙 時(shí)間: 2025-3-29 22:53 作者: 單獨(dú) 時(shí)間: 2025-3-30 02:29
https://doi.org/10.1007/978-3-319-98437-7output model is constructed using a multi-layered perceptron network and a closed loop control strategy is adopted using a genetic algorithm to control inflation through the manipulation of interest rates. Given the historical inflation rate data, a control scheme is used to determine the interest r作者: investigate 時(shí)間: 2025-3-30 07:31 作者: Ige326 時(shí)間: 2025-3-30 08:53 作者: 眉毛 時(shí)間: 2025-3-30 14:58
https://doi.org/10.1007/978-1-4471-5010-7Artificial Intelligence; Bayesian; Boolean Reasoning; Causality; Computational Intelligence; Decision Rul
