scholarly journals Application of Neural Network Model Based on Multispecies Evolutionary Genetic Algorithm to Planning and Design of Diverse Plant Landscape

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuqiang Wu ◽  
Weiwei Guo ◽  
Dinghai Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Plant Species ◽  
Network Model ◽  
Neural Network Model ◽  
Landscape Planning ◽  
Neural Network Modeling ◽  
Planning And Design ◽  
Evolutionary Genetic ◽  
Diverse Plant

In order to explore the feasibility of applying neural network model to landscape planning, based on the multispecies evolutionary genetic algorithm, a neural network model is proposed in this paper for the system design of diverse plant landscape planning. From the perspective of plant species diversity, this paper discusses landscape planning based on a neural network model. This landscape plan involves more than 180 plant species, mainly shrubs, fungi, and so on. The application of multispecies evolutionary genetic algorithm to landscape planning and design and the application of gene level coding and multispecies parallel evolution strategy to the evolutionary design of neural network have guiding significance for plant landscape planning and design. Compared with the traditional neural network modeling method and genetic algorithm, the proposed method has the advantages of wide network structure search space and simple algorithm calculation and design, independent of specific application background, and has strong application and promotion value. This method makes the model performance evaluation index more comprehensive and accurate and the model solution more reasonable. At the same time, combined with the specific status and corresponding changes of various plants in each season, this paper designs a targeted plan to rationally plan the specific spatial layout of the plant landscape and the combination of different types of plant landscapes, so as to effectively improve the quality of the landscape.

Neural network modeling of the wear process of a carbide cutting tool

2021 ◽  
Author(s):  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Cutting Tool ◽  
Tool Material ◽  
Neural Network Modeling ◽  
Wear Process ◽  
Carbide Cutting Tool ◽  
Model Dynamics ◽  
Processing Modes

A neural network model of the wear process of a carbide cutting tool is proposed. This model is considered influence of the cutting dynamics on the tool. The dependence of the wear rate on the processing modes and properties of the processed and tool material is shown. Keywords cutting tool; neural network model; dynamics of the cutting process; wear

SEARCHING BY METOD OF NEURAL NETWORK MODELING OF CONDENSED BENZIMIDAZOLE DERIVATIVES WITH MULTI-TARGET RAGE-INHIBITORY ACTIVITY

2020 ◽  
pp. 248-249
Author(s):  
O. Zhukovskaya ◽  
A. Spasov ◽  
A. Morkovnik ◽  
A. Kochetkov
Keyword(s):  
Neural Network ◽  
Virtual Screening ◽  
Network Model ◽  
Neural Network Model ◽  
Inhibitory Activity ◽  
Network Modeling ◽  
Inhibitory Effect ◽  
Benzimidazole Derivatives ◽  
Neural Network Modeling

Using a multitarget neural network model of RAGE-inhibitory activity, a consensus virtual screening of a library of new condensed benzimidazole derivatives was performed. Compounds with a essential RAGE-inhibitory effect have been found.

scholarly journals Determining optimal combination of roller compacted concrete pavement mixture containing recycled asphalt pavement and crumb rubber using hybrid artificial neural network–genetic algorithm method considering energy absorbency approach

2017 ◽  
Vol 44 (11) ◽  
pp. 945-955 ◽  
Author(s):  
Mansour Fakhri ◽  
Ershad Amoosoltani ◽  
Mona Farhani ◽  
Amin Ahmadi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Fitness Function ◽  
Crumb Rubber ◽  
Concrete Pavement ◽  
Genetic Operators ◽  
Efficiency Criterion ◽  
Roller Compacted Concrete

The present study investigates the effectiveness of evolutionary algorithms such as genetic algorithm (GA) evolved neural network in estimating roller compacted concrete pavement (RCCP) characteristics including flexural and compressive strength of RCC and also energy absorbency of mixes with different compositions. A real coded GA was implemented as training algorithm of feed forward neural network to simulate the models. The genetic operators were carefully selected to optimize the neural network, avoiding premature convergence and permutation problems. To evaluate the performance of the genetic algorithm neural network model, Nash-Sutcliffe efficiency criterion was employed and also utilized as fitness function for genetic algorithm which is a different approach for fitting in this area. The results showed that the GA-based neural network model gives a superior modeling. The well-trained neural network can be used as a useful tool for modeling RCC specifications.

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

2020 ◽  
Vol 34 (3) ◽  
pp. 430-443
Author(s):  
A. Saravanan ◽  
J. Jerald ◽  
A. Delphin Carolina Rani
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Network Models ◽  
Manufacturing Cost ◽  
Genetic Operators ◽  
Neural Network Models ◽  
Data Set ◽  
The Neural Network

AbstractThe objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.

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