Research of Marine Diesel Engine’s State Prediction Based on Evolutionary Neural Network and Spectrometric Analysis

2011 ◽  
Vol 346 ◽  
pp. 339-345 ◽  
Author(s):  
Hai Jun Wei ◽  
Guo You Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Diesel Engine ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lubricating Oil ◽  
Spectrometric Analysis ◽  
Marine Diesel Engine ◽  
Marine Diesel

In this paper, an evolutionary neural networks model is proposed to predict the content of metal elements contained in marine diesel engine lubricating oil, by fusing genetic algorithms (GAs) and error back propagation neural network (BPNN) to offset the demerits of one paradigm by the merits of another. The input data of metal content was detected by spectrometric analysis. Genetic algorithms are used to globally optimize the weights and threshold of BP neural networks. Moreover, one case study was presented to illustrate the proposed method. The prediction accuracy of the novel method is compared with that of only BPNN method to illustrate the feasibility and effectiveness of the proposed method. The relative error on average of results is 1.52%, it can meet the precision request of state detecting in marine diesel engine.

Prediction of PAHs Emitted from Marine Diesel Engine Using Artificial Neural Networks Combining Genetic Algorithms

2014 ◽  
Vol 599-601 ◽  
pp. 1233-1236
Author(s):  
Ming Yu Wang ◽  
Shao Jun Zhang ◽  
Xiao Zhang
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Diesel Engine ◽  
Network Performance ◽  
Experimental Studies ◽  
Marine Diesel Engine ◽  
Marine Diesel ◽  
Electronic Parameters ◽  
Artificial Neural

Experimental studies on operating a marine diesel engine to determine the performance map under different working conditions need to consume a lot of money and labor. To solve this problem, a mathematical model based on Artificial Neural Networks (ANNs) combined genetic algorithms (GA) to predicate the performance emissions of the marine diesel engine is firstly reported in this paper. The predicted result showed that the network performance is sufficient for all target emission outputs. The input layer without transfer function consisted of 11 neurons is used, and output layer predicted 16 polycyclic aromatic hydrocarbons (PAHs). Electronic parameters such as VIC, SOI, CRP, NUN, VEO and VEC have influences on the PAHs emissions. The actual data obtained from the diesel is well agreed with the predicted data. The usage of ANNs is highly recommended to predict engine emissions instead of having to undertake complex and time-consuming experimental studies.

Research on the Speed of Diesel Engine Based on Improved BP Neural Network Controller

2013 ◽  
Vol 281 ◽  
pp. 105-111 ◽  
Author(s):  
Yong Shi ◽  
Lian Yu Zhang ◽  
Jun Sun ◽  
Hong Guang Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Diesel Engine ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Adaptive Controller ◽  
Regulation System ◽  
Marine Diesel Engine ◽  
Neural Network Controller ◽  
Marine Diesel

Marine diesel engine is of characteristics of non-linear and time-invariant, so it is difficult to be controlled with traditional PID controller. An adaptive controller based on back-propagation (BP) neural networks was put forwarded for marine diesel engine speed control system, where two neural networks are proposed to control the position loop and speed loop. The adaptive controller was improved was improved via introducing relative error in target evaluation function of the BP neural network, and obtain sensitivity function of diesel engine output with respect to its input using a differential equation. The controller has self-learning and adaptive capacity. It can also optimize the PID controller parameters online. The controller was experimentally evaluated on rack position actuator of marine diesel engine simulated based on a diesel hardware-in-loop system of dSPACE. Finally, tests on a diesel engine demonstrated that the controller can satisfy the transient and steady demands of speed regulation system.

scholarly journals Automatical Knowledge Representation of Logical Relations by Dynamical Neural Network

2017 ◽  
Vol 26 (4) ◽  
pp. 625-639 ◽  
Author(s):  
Gang Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Knowledge Representation ◽  
Network Structure ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ann Model ◽  
Functional Approximation ◽  
Dynamical Neural Network

AbstractCurrently, most artificial neural networks (ANNs) represent relations, such as back-propagation neural network, in the manner of functional approximation. This kind of ANN is good at representing the numeric relations or ratios between things. However, for representing logical relations, these ANNs have disadvantages because their representation is in the form of ratio. Therefore, to represent logical relations directly, we propose a novel ANN model called probabilistic logical dynamical neural network (PLDNN). Inhibitory links are introduced to connect exciting links rather than neurons so as to inhibit the connected exciting links conditionally to make them represent logical relations correctly. The probabilities are assigned to the weights of links to indicate the belief degree in logical relations under uncertain situations. Moreover, the network structure of PLDNN is less limited in topology than traditional ANNs, and it is dynamically built completely according to the data to make it adaptive. PLDNN uses both the weights of links and the interconnection structure to memorize more information. The model could be applied to represent logical relations as the complement to numeric ANNs.

Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms

2005 ◽  
Vol 9 (3) ◽  
pp. 290-296 ◽  
Author(s):  
Benyamin Kusumoputro ◽  
◽  
Teguh P. Arsyad
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Multilayer Perceptron ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Odor Recognition ◽  
Fuzzy Neural ◽  
Odor Mixtures

Recognizing odor mixtures is rather difficult in artificial odor recognition system, especially when the number of sensors is limited. Classification is further hampered if the number of unlearned odor mixtures classes is increased. We developed a fuzzy-neuro multilayer perceptron as a pattern classifier and compared its recognition with that of the Probabilistic Neural Network and Back-propagation Neural Network. To enhance the recognition capability of the system, we then optimized fuzzy-neuro multilayer perceptron topology by deleting its weak weight connections using Genetic Algorithms. Experimental results show that the optimized fuzzy-neuro multilayer perceptron has the highest recognition in 18 classes of two-mixture odors with almost 98.2% when using hardware with 16 sensors, compared to 83.3% when using 8 sensors.

Coal Mine Disaster Warning Internet of Things Intrusion Detection System Based on Back Propagation Neural Network Improved by Genetic Algorithms

2013 ◽  
Vol 441 ◽  
pp. 343-346 ◽  
Author(s):  
Ying Hu ◽  
Li Min Sun ◽  
Sheng Chen Yu ◽  
Jiang Lan Huang ◽  
Xiao Ju Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Internet Of Things ◽  
Coal Mine ◽  
Intrusion Detection System ◽  
Detection System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Threshold Values ◽  
Data Set

In order to improve the detection rate of intruders in coal mine disaster warning internet of things, and to solve the problem that the back propagate neural network (BPNN) is invalid when these initial weight and threshold values of BPNN are chosen impertinently, Genetic Algorithms (GA) s characteristic of getting whole optimization value was combined with BPNNs characteristic of getting local precision value with gradient method. After getting an approximation of whole optimization value of weight and threshold values of BPNN by GA, the approximation was used as first parameter of BPNN, to train (educate) the BPNN again (in other words, learning). The educated BPNN was used to recognize intruders in internet of things. Experiment results shown that this method was useful and applicable, and the detection right rate of intruders was above 95% for the KDD CUP 1999 data set.

Prediction of Human Responses to Dairy Odor Using an Electronic Nose and Neural Networks

2018 ◽  
Vol 61 (2) ◽  
pp. 399-409 ◽  
Author(s):  
Fangle Chang ◽  
Paul Heinemann
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Electronic Nose ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Sources Of Information ◽  
Independent Validation ◽  
Quantitative Measurements ◽  
Resilient Back Propagation ◽  
Human Assessment

Abstract. Odor emitted from dairy operations may cause negative reactions by farm neighbors. Identification and evaluation of such malodors is vital for better understanding of human response and methods for mitigating effects of odors. The human nose is a valuable tool for odor assessment, but using human panels can be costly and time-consuming, and human evaluation of odor is subjective. Sensing devices, such as an electronic nose, have been widely used to measure volatile emissions from different materials. The challenge, though, is connecting human assessment of odors with the quantitative measurements from instruments. In this work, a prediction system was designed and developed to use instruments to predict human assessment of odors from common dairy operations. The model targets are the human responses to odor samples evaluated using a general pleasantness scale ranging from -11 (extremely unpleasant) to +11 (extremely pleasant). The model inputs were the electronic nose measurements. Three different neural networks, a Levenberg-Marquardt back-propagation neural network (LMBNN), a scaled conjugate gradient back-propagation neural network (CGBNN), and a resilient back-propagation neural network (RPBNN), were applied to connect these two sources of information (human assessments and instrument measurements). The results showed that the LMBNN model can predict human assessments with accuracy as high as 78% within a 10% range and as high as 63% within a 5% range of the targets in independent validation. In addition, the LMBNN model performed with the best stability in both training and independent validation. Keywords: Animal production, Hedonic tone, Olfactometric models.

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