Prediction Model of Weekly Retail Price for Eggs Based on Chaotic Neural Network

It is always hard to draw on the experience of completed projects to predict engineering cost, and the nonlinear characteristic of the influence factors of engineering cost increases the difficulty of prediction. Less efforts and higher accuracy are the objects pursued by related researchers. In this paper, the Cost Significant theorem is applied to simplify computing and the chaotic neural network is used to improve accuracy. The prediction model is rooted from the nonlinear dynamic chaotic system theory and two techniques employed are phase space reconstruction and chaotic neural network construction. The experiment results indicate that the model is suitable for estimating short-term engineering investment and the prediction accuracy is improved.

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Prediction Method of Main Parameters in Internal Mixer Based on Chaotic Neural Network

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.501.398 ◽

2012 ◽

Vol 501 ◽

pp. 398-401

Author(s):

Su Zhen Huang ◽

Chuan Sheng Wang

Keyword(s):

Neural Network ◽

Prediction Model ◽

Rbf Neural Network ◽

Prediction Method ◽

Chaotic Mixing ◽

Mixing Process ◽

Training Process ◽

Chaotic Neural Network ◽

Internal Mixing ◽

Internal Mixer

The chaotic mixing prediction model was established based on RBF neural network and the chaotic mixing process of internal mixing. Training process indicates the method has powerful approaching ability, classing ability and convergence. Actual experiment results verified validity and veracity of this method.

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The short-term life prediction model of gearbox based on chaotic neural network

2011 IEEE 18th International Conference on Industrial Engineering and Engineering Management ◽

10.1109/icieem.2011.6035367 ◽

2011 ◽

Author(s):

Xiao-hui Chen ◽

Li-ming Cui ◽

Jun-xing Li

Keyword(s):

Neural Network ◽

Prediction Model ◽

Life Prediction ◽

Chaotic Neural Network ◽

Short Term ◽

Life Prediction Model

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Stock Market Prediction using Data Mining Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206290 ◽

2020 ◽

pp. 310-313

Author(s):

Karunesh Makker ◽

Prince Patel ◽

Hrishikesh Roy ◽

Sonali Borse

Keyword(s):

Neural Network ◽

Stock Market ◽

Prediction Model ◽

Efficient Market Hypothesis ◽

Deterministic System ◽

Vast Number ◽

Factors Influencing ◽

Number Of Factors ◽

Using Data ◽

Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

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