Development of Artificial Neural Networks Trained by Heuristic Algorithms for Prediction of Exhaust Emissions and Performance of a Diesel Engine Fuelled with Biodiesel Blends

2019 ◽  
pp. 253-275
Author(s):  
Quang Hung Do ◽  
Tran Trong Tuan ◽  
Luu Thi Thu Ha ◽  
Thi Thanh Hang Doan ◽  
Thi Van Anh Nguyen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Diesel Engine ◽  
Heuristic Algorithms ◽  
Exhaust Emissions ◽  
Biodiesel Blends ◽  
Artificial Neural ◽  
And Performance ◽  
Emissions And Performance

Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends

2012 ◽  
Vol 92 ◽  
pp. 769-777 ◽  
Author(s):  
Harun Mohamed Ismail ◽  
Hoon Kiat Ng ◽  
Cheen Wei Queck ◽  
Suyin Gan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Diesel Engine ◽  
Biodiesel Blends ◽  
Light Duty ◽  
Artificial Neural

Heuristic Approach Performances for Artificial Neural Networks Training

2021 ◽  
pp. 382-401
Author(s):  
Kerim Kürşat Çevik
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Heuristic Algorithms ◽  
Optimal Algorithm ◽  
Training System ◽  
Heuristic Approach ◽  
Bp Algorithm ◽  
Breast Cancer Dataset ◽  
Artificial Neural ◽  
And Performance

This chapter aimed to evaluate heuristic approach performances for artificial neural networks (ANN) training. For this purpose, software that can perform ANN training application was developed using four different algorithms. First of all, training system was developed via back propagation (BP) algorithm, which is the most commonly used method for ANN training in the literature. Then, in order to compare the performance of this method with the heuristic methods, software that performs ANN training with genetic algorithm (GA), particle swarm optimization (PSO), and artificial immunity (AI) methods were designed. These designed software programs were tested on the breast cancer dataset taken from UCI (University of California, Irvine) database. When the test results were evaluated, it was seen that the most important difference between heuristic algorithms and BP algorithm occurred during the training period. When the training-test durations and performance rates were examined, the optimal algorithm for ANN training was determined as GA.

scholarly journals Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria-Based Concrete

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Aref M. al-Swaidani ◽  
Waed T. Khwies
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Supplementary Cementitious Material ◽  
Natural Pozzolans ◽  
Long Time ◽  
Ann Models ◽  
Artificial Neural ◽  
And Performance ◽  
And Training

Numerous volcanic scoria (VS) cones are found in many places worldwide. Many of them have not yet been investigated, although few of which have been used as a supplementary cementitious material (SCM) for a long time. The use of natural pozzolans as cement replacement could be considered as a common practice in the construction industry due to the related economic, ecologic, and performance benefits. In the current paper, the effect of VS on the properties of concrete was investigated. Twenty-one concrete mixes with three w/b ratios (0.5, 0.6, and 0.7) and seven replacement levels of VS (0%, 10%, 15%, 20%, 25%, 30%, and 35%) were produced. The investigated concrete properties were the compressive strength, the water permeability, and the concrete porosity. Artificial neural networks (ANNs) were used for prediction of the investigated properties. Feed-forward backpropagation neural networks have been used. The ANN models have been established by incorporation of the laboratory experimental data and by properly choosing the network architecture and training processes. This study shows that the use of ANN models provided a more accurate tool to capture the effects of five parameters (cement content, volcanic scoria content, water content, superplasticizer content, and curing time) on the investigated properties. This prediction makes it possible to design VS-based concretes for a desired strength, water impermeability, and porosity at any given age and replacement level. Some correlations between the investigated properties were derived from the analysed data. Furthermore, the sensitivity analysis showed that all studied parameters have a strong effect on the investigated properties. The modification of the microstructure of VS-based cement paste has been observed, as well.

Application of Artificial Neural Networks in Predicting the Degradation of Tram Tracks Using Maintenance Data

2020 ◽  
pp. 845-864
Author(s):  
Sara Moridpour ◽  
Ehsan Mazloumi ◽  
Reyhaneh Hesami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Asset Management ◽  
Management Strategies ◽  
Operating Cost ◽  
Light Rail ◽  
Ann Model ◽  
Track Geometry ◽  
Artificial Neural ◽  
And Performance

The increase in number of passengers and tramcars will wear down existing rail structures faster. This is forcing the rail infrastructure asset owners to incorporate asset management strategies to reduce total operating cost of maintenance whilst improving safety and performance. Analysing track geometry defects is critical to plan a proactive maintenance strategy in short and long term. Repairing and maintaining the correctly selected tram tracks can effectively reduce the cost of maintenance operations. The main contribution of this chapter is to explore the factors influencing the degradation of tram tracks (light rail tracks) using existing geometric data, inspection data, load data and repair data. This chapter also presents an Artificial Neural Networks (ANN) model to predict the degradation of tram tracks. Predicting the degradation of tram tracks will assist in understanding the maintenance needs of tram system and reduce the operating costs of the system.

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