scholarly journals Nonlinear analysis and prediction of soybean futures

2021 ◽  
Vol 67 (No. 5) ◽  
pp. 200-207
Author(s):  
Tao Yin ◽  
Yiming Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Local Approximation ◽  
Gaussian Kernel ◽  
Test Results ◽  
Agricultural Futures ◽  
Existence Test ◽  
Benchmark System ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

We use chaotic artificial neural network (CANN) technology to predict the price of the most widely traded agricultural futures – soybean futures. The nonlinear existence test results show that the time series of soybean futures have multifractal dynamics, long-range dependence, self similarity, and chaos characteristics. This also provides a basis for the construction of a CANN model. Compared with the artificial neural network (ANN) structure as our benchmark system, the predictability of CANN is much higher. The ANN is based on Gaussian kernel function and is only suitable for local approximation of nonstationary signals, so it cannot approach the global nonlinear chaotical hidden pattern. Improving the prediction accuracy of soybean futures prices is of great significance for investors, soybean producers, and decision makers.

scholarly journals Prediction of the Dynamic Stiffness of Resilient Materials using Artificial Neural Network (ANN) Technique

2019 ◽  
Vol 9 (6) ◽  
pp. 1088 ◽  
Author(s):  
Changhyuk Kim ◽  
Jung-Yoon Lee ◽  
Moonhyun Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Residential Buildings ◽  
Dynamic Stiffness ◽  
Test Results ◽  
High Rise ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Sound Reduction ◽  
Reduction Capacity

High-rise residential buildings are constructed in countries with high population density in response to the need to utilize small development areas. As many high-rise buildings are being constructed, issues of floor impact sound tend to occur in buildings. In general, resilient materials are implemented between the slab and the finishing mortar to control the floor impact sound. Various mechanical properties of resilient materials can affect the floor impact sound. To investigate the impact sound reduction capacity, various experimental tests were conducted. The test results show that the floor impact sound reduction capacity has a close relationship with the dynamic stiffness of resilient materials. A total of six different kinds of resilient materials were loaded under four loading conditions. The test results show that loading time, loading, and material properties influence the change in dynamic stiffness. Artificial neural network (ANN) technique was implemented to obtain the responses between the deflection and dynamic stiffness. Three different algorithms were considered in the ANN models and the trained results were analyzed based on the root mean square error. The feasibility of using the ANN technique was verified with a high and consistent level of accuracy.

scholarly journals Prediction of Waste Heat Energy Recovery Performance in a Naturally Aspirated Engine Using Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Safarudin Gazali Herawan ◽  
Abdul Hakim Rohhaizan ◽  
Azma Putra ◽  
Ahmad Faris Ismail
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Waste Heat ◽  
Simulation Method ◽  
Spark Ignition Engine ◽  
Test Results ◽  
Simulation Test ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Heat Energy Recovery

The waste heat from exhaust gases represents a significant amount of thermal energy, which has conventionally been used for combined heating and power applications. This paper explores the performance of a naturally aspirated spark ignition engine equipped with waste heat recovery mechanism (WHRM). The experimental and simulation test results suggest that the concept is thermodynamically feasible and could significantly enhance the system performance depending on the load applied to the engine. The simulation method is created using an artificial neural network (ANN) which predicts the power produced from the WHRM.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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