Implementation of Efficient Multilayer Perceptron ANN Neurons on Field Programmable Gate Array Chip

Artificial Neural Network is widely used to learn data from systems for different types of applications. The capability of different types of Integrated Circuit (IC) based ANN structures also depends on the hardware backbone used for their implementation. In this work, Field Programmable Gate Array (FPGA) based Multilayer Perceptron Artificial Neural Network (MLP-ANN) neuron is developed. Experiments were carried out to demonstrate the hardware realization of the artificial neuron using FPGA. Two different activation functions (i.e. tan-sigmoid and log-sigmoid) were tested for the implementation of the proposed neuron. Simulation result shows that tan-sigmoid with a high index (i.e. k >= 40) is a better choice of sigmoid activation function for the harware implemetation of a MLP-ANN neuron

Application of Artificial Neural Network (ANN) to Predict Soil Organic Matter Using Remote Sensing Data in Two Ecosystems

Concern over global problems induced by rising CO2 has prompted attention on the role of forests and pastures as carbon ‘storage’ because forests and pastures store a large amount of carbon in vegetation biomass and soil. Soil organic matter (SOM) plays a critical role in soil quality and has the potential to cost-effectively mitigate the detrimental effects of rising atmospheric CO2 and other greenhouse gas emissions that cause global warming and climate change(Causarano-Medina, 2006). SOM, an important source of plant nutrients is itself influenced by land use, soil type, parent material, time, climate and vegetation (Loveland &Webb, 2003). Important climatic factors influencing SOM include rainfall and temperature. Within the same isotherm, the SOM content increases with increase in rainfall regime. For the same isohyet, the SOM content...............

Artificial Intelligence Tools to Better Understand Seed Dormancy and Germination

Despite a large number of publications available, the control mechanisms of seed dormancy and germination are far to be fully understood. Seed dormancy and germination are very complex biological processes and because they involve multiple factors (physiological, mechanical, and environmental) and their nonlinear interactions. This explains why extremely little variations on some of those factors and in the way they interact caused enormous variation in the obtained results. Multifactorial process like these can be modeled using computer-based tools to predict better results. In this chapter, some basic concepts relative to seed dormancy and germination and the main factors (physiological, involved in seed dormancy, particularly dormancy-inducers and dormancy-breakers, and seed germination) are reviewed. In the next two, we describe the use of artificial intelligence computer-based models to better understand the physiological mechanisms of seed dormancy (how dormancy is controlled and how can be released) and seed germination. Finally, some applications of artificial neural networks, fuzzy logic, and genetic algorithms to elucidate critical factors and predict optimal condition for seed dormancy-breaking and germination are given as examples of the utility of this powerful computer-based tools

Artificial intelligence tools to better understand seed dormancy and germination

Despite a large number of publications available, the control mechanisms of seed dormancy and germination are far to be fully understood. Seed dormancy and germination are very complex biological processes and because they involve multiple factors (physiological, mechanical, and environmental) and their nonlinear interactions. This explains why extremely little variations on some of those factors and in the way they interact caused enormous variation in the obtained results. Multifactorial process like these can be modeled using computer-based tools to predict better results. In this chapter, some basic concepts relative to seed dormancy and germination and the main factors (physiological, involved in seed dormancy, particularly dormancy-inducers and dormancy-breakers, and seed germination) are reviewed. In the next two, we describe the use of artificial intelligence computer-based models to better understand the physiological mechanisms of seed dormancy (how dormancy is controlled and how can be released) and seed germination. Finally, some applications of artificial neural networks, fuzzy logic, and genetic algorithms to elucidate critical factors and predict optimal condition for seed dormancy-breaking and germination are given as examples of the utility of this powerful computer-based tools

News Article Classification using Kolmogorov Complexity Distance Measure and Artificial Neural Network

News article classification is a recently growing area of interest in text classification because of its associated multiple matching categories. However, the weak reliability indices and ambiguities associated with state-of-the-art classifiers often employed make success in this domain very limited. Also, the high sensitivity and large disparity in performance results of classifiers to the varying nature of real-world datasets make the need for comparative evaluation inevitable. In this paper, the accuracy and computational time efficiency of the Kolmogorov Complexity Distance Measure (KCDM) and Artificial Neural Network (ANN) were experimentally evaluated for a prototype large dimensional news article classification problem. 2000 News articles from a dataset of 2225 British Broadcasting Corporation (BBC) news documents (including examples from sport, politics, entertainment, education and technology, and business) were used for categorical testing purposes. Porter’s algorithm was used for word stemming after tokenization and stop-words removal, and a Normalized Term Frequency–Inverse Document Frequency (NTF-IDF) technique was adopted for feature extraction. Experimental results revealed that ANN performs better in terms of accuracy while the KCDM produced better results than ANN in terms of computational time efficiency

An Experimental Setup to Measure Argon Leak Rate Through Barriers for Static and Rotational Motion

As a greenhouse gas, methane significantly impacts global warming and air pollution. Oil and gas production, crude oil transportation, refining, and natural gas processing, transportation, and distribution are considered the main sources of methane emission in the oil and gas industry. Specifically, valves, joints, and moving parts where barriers were used have a magnificent role in methane emission. To address this problem, International Standard Organization(ISO) through ISO 15848 suggested that the oil and gas industry test the fugitive emission on their products with helium as a testing gas. Although, helium is an inert gas and its specifications make it an ideal gas for emission tests in the oil and gas industry, difficulty in production, transportation, high price, and global helium shortage lead us to find an alternative material. In this study, a setup was built to test argon emission in 25C, 121C, and 204C and pressure ranges of 600, 2250, 3750, 6750, and 10,000 psi on v-rings for static and rotational shafts with 2 rpm and 10 rpm. Experimental results were used to generate a machine learning model. Finally, a general polynomial formula was presented based on the machine learning model for static and rotational shafts with 2 rpm and 10 rpm. Results show the impact of temperature and rotational shaft’s speed on leak rate is significant. This study’s results apply to the valve, compressors, and any dynamic seal test process. Generally, the applications of this study can be to reduce the cost of the production and leak rate tests in rotational equipment design for the oil and gas industry

A novel strategy for speed up training for back propagation algorithm via dynamic adaptive the weight training in artificial neural network

The drawback of the Back Propagation (BP) algorithm is slow training and easily convergence to the local minimum and suffers from saturation training.To overcome those problems, we created a new dynamic function for each training rate and momentum term.In this study, we presented the (BPDRM) algorithm, which training with dynamic training rate and momentum term. Also in this study, a new strategy is proposed, which consists of multiple steps to avoid inflation in the gross weight when adding each training rate and momentum term as a dynamic function.In this proposed strategy, fitting is done by making a relationship between the dynamic training rate and the dynamic momentum.As a result, this study placed an implicit dynamic momentum term in the dynamic training rate.This αdmic = f(1/η_dmic ).This procedure kept the weights as moderate as possible (not to small or too large).The 2-dimensional XOR problem and buba data were used as benchmarks for testing the effects of the ‘new strategy’. All experiments were performed on Matlab software (2012a).From the experiment’s results, it is evident that the dynamic BPDRM algorithm provides a superior performance in terms of training and it provides faster training compared to the (BP) algorithm at same limited error