神经网络在Android手机中有效预测应用的细致训练

Y. Karunakar, A. Kuwadekar
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引用次数: 1

摘要

在当今世界,训练有素的神经网络的使用已经找到了一个多样化的应用领域。在大多数发展中国家,投资股票虽然有风险因素,但却是最赚钱的快钱方式。这导致了各种金融市场和投资模式的发展。布莱克-斯科尔斯模型为股票市场研究开辟了一个新的领域。该模型推导出偏微分方程,其解Black-Scholes公式被广泛用于欧式期权的定价。“基于神经网络的股票价格预测模型”的目的是开发一个基于手持Android手机的未来股票价格预测模型。它将通过使用人工智能中的一个概念(8)来开发,“人工神经网络(ANNs)已经迅速普及。它们是人工智能自适应软件系统,受到生物神经网络工作原理的启发。使用它们是因为它们可以学习检测数据中的复杂模式。用数学术语来说,它们是通用函数逼近器,这意味着给定正确的数据并正确配置;它们可以捕获和建模任何输入-输出关系。这不仅消除了人为解释图表或生成进入/退出信号的一系列规则的需要,而且还为基本面分析提供了一座桥梁,因为基本面分析中使用的变量可以用作输入。由于人工神经网络本质上是非线性统计模型,它们的准确性和预测能力可以在数学和经验上进行测试。在各种研究中,作者声称,当与基于规则的专家系统相结合时,用于生成各种技术和基本输入的交易信号的神经网络明显优于买入持有策略以及传统的线性技术分析方法。索引术语:Black-Scholes模型,神经网络,股票市场,反向传播,模式识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Punctilious Training of Neural Networks for Efficacious Applications of Predictions in Android Phones
The use of trained Neural networks has found a variegated field of applications in the present world. In most of the developing countries, investing in stocks, albeit the risk factor is the most lucrative way of earning quick bucks. This has lead to the development of various models for financial markets and investment. Black-Scholes model opened a new domain for research in the field of stock markets. The model develops partial differential equations whose solution, the Black-Scholes formula, is widely used in the pricing of European-style options. The Aim of "Neural Network Based Stock Price Forecasting Model" is to develop a Model which will be used to Forecast Future Stock Prices using handheld Android Mobile phones. It will be developed by using one of the Concepts in Artificial Intelligence (8), "Artificial neural networks (ANNs) have rapidly grown in popularity. They are artificial intelligence adaptive software systems that have been inspired by how biological neural networks work. They are used because they can learn to detect complex patterns in data. In mathematical terms, they are universal function approximators, meaning that given the right data and configured correctly; they can capture and model any input-output relationships. This not only removes the need for human interpretation of charts or the series of rules for generating entry/exit signals, but also provides a bridge to fundamental analysis, as the variables used in fundamental analysis can be used as input. As ANNs are essentially non-linear statistical models, their accuracy and prediction capabilities can be both mathematically and empirically tested. In various studies, authors have claimed that neural networks used for generating trading signals given various technical and fundamental inputs have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods when combined with rule-based expert systems. Index Terms— Black-Scholes model, Neural Networks, Stock markets, Backpropogation, Pattern recognition.
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