scholarly journals Design of the system of automated generation of poetry works

2021 ◽  
Vol 3 (2) ◽  
pp. 01-14
Author(s):  
T. P. Diak ◽  
◽  
Yu. I. Hrytsiuk ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Computational Linguistics ◽  
Optimal Solution ◽  
Neural Network Learning ◽  
Automated Generation ◽  
Software Application ◽  
Software Products ◽  
The Neural Network

Features of designing a system of automated generation of poetic works, which opens up new opportunities for artistic speech and show business, especially the preparation of poems and songs have been considered. Quite often lyrics without special content become successful due to the lack of complex plots, as well as due to the unobtrusiveness and ease of perception by listeners. Well-known literature sources and available software products that can generate poetic works by combining different methods and algorithms are analyzed. It has been established that none of them is able to ensure the content and uniqueness of the poetic work at the same time, especially in the Ukrainian language. The existing approaches to the generation of poetic works are analysed, among which the relevant is a method based on templates, generation and testing, evolutionary algorithms and the method based on specific cases. Peculiarities of generating poetical works, first of all rhyming rules, types of strophes, poetic rhythms and sizes have been investigated. An approach to automated generation of poetic works using evolutionary algorithms and a method based on specific cases have been developed. Their combination resembles a sequence of actions for creative personalities when creating poems or writing lyrics. Peculiarities of neural network organization for automated generation of poetic works have been considered. It is proposed to perform neural network training using the method of inverse propagation and using a genetic algorithm. The principle of operation of algorithms for finding optimal solutions which contain such consecutive stages as initialization, evaluation of solutions, population selection, evolution of solutions, is analysed. Their interaction and various opportunities for neural network learning have been investigated in detail. An algorithm has been developed according to which the software application will analyse the poetic works offered by the user and generate new variants of it on the basis received from the neural network of logically connected words or lines of the verse in the poem. The user can edit both the components of the poem and the generated poetic works, and thus can train the neural network. The specification of requirements to the software application has been developed, the basic requirements to the user interface are defined, and also potential classes of users who will use it are established. Keywords: computational linguistics; Artificial Intelligence; neural network; genetic algorithm; optimal solution.

Optimization Algorithms

2014 ◽  
pp. 182-199 ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Learning Enhancement ◽  
Swarm Optimization ◽  
Neural Network Learning ◽  
The Neural Network ◽  
Intelligent Behavior ◽  
Definition Of

In this chapter, the basic definition of Genetic Algorithm (GA) and some of the main operations applied in GA are explained. In addition, Swarm Intelligence (SI) is briefly explained as the new branch of intelligent behavior of nature phenomena. Although PSO has been explained in past chapters, this chapter explains PSO in detail and an example of the way PSO works is provided for better understanding. Some of the differences of Particle Swarm Optimization (PSO) and GA are provided and readers will learn how to use GA and PSO for training the neural network. The experiments and contents in this chapter are from the study by Nuzly (2006) in her thesis entitled “Particle Swarm Optimization for Neural Network Learning Enhancement”.

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 723-728 ◽  
Author(s):  
Chenhao Niu ◽  
Xiaomin Xu ◽  
Yan Lu ◽  
Mian Xing
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Load Forecasting ◽  
Optimal Solution ◽  
Electric Power System ◽  
Local Optimum ◽  
Good Prediction ◽  
Ant Colony Optimization Algorithm ◽  
The Neural Network ◽  
Power Load

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

scholarly journals Optimizing the hardness of SLA printed objects by using the neural network and genetic algorithm

2019 ◽  
Vol 38 ◽  
pp. 117-124
Author(s):  
Guang Hu ◽  
Zhi Cao ◽  
Michael Hopkins ◽  
Conor Hayes ◽  
Mark Daly ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
The Neural Network

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

The Research of Intrusion Detection Based on Intelligentization

2014 ◽  
Vol 490-491 ◽  
pp. 1588-1591
Author(s):  
Liang Zhang ◽  
Hao Yue Sun ◽  
Guo Lv ◽  
Xiao Lu Sun
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Convergence Speed ◽  
Global Property ◽  
Detection Accuracy ◽  
Accuracy Rate ◽  
The Neural Network

In this paper, the intelligentized way is applied to detecting anomaly intrusion. Based on the global property of genetic algorithm and the locality of neural network, this method effectively improves the convergence speed of the network and the detection accuracy rate. It not only avoids the defect of the neural network, but also improves the precision.

GTAW Procedure Expert System Based on Neural Network

2013 ◽  
Vol 455 ◽  
pp. 425-430 ◽  
Author(s):  
Xue Wu Wang ◽  
Shang Yong Yang
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Expert Knowledge ◽  
Welding Process ◽  
Learning Ability ◽  
Welding Parameters ◽  
Neural Network Learning ◽  
The Neural Network ◽  
Function Design ◽  
Welding Procedure

Intelligent procedure expert system was developed to select appropriate GTAW procedure in this paper. First, the function design and implementation methods of the welding procedure expert system were introduced. The expert system can present the welding procedure card, multimedia display of welding process, and output function to makes the data sharing more convenient. Then, the database design of the welding procedure expert system based on C/S mode was presented where the expert knowledge was stored. At last, the neural network model was established to realize procedure selection based on the neural network learning ability and the welding case from the database. With the BPNN model, the welding parameters can be obtained based on the input welding conditions.

HIERARCHICAL GENETIC ALGORITHM FOR NEAR-OPTIMAL FEEDFORWARD NEURAL NETWORK DESIGN

2002 ◽  
Vol 12 (01) ◽  
pp. 31-43 ◽  
Author(s):  
GARY YEN ◽  
HAIMING LU
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Cost Function ◽  
Design Procedure ◽  
Optimal Solution ◽  
Traditional Learning ◽  
Feed Forward Neural Network ◽  
Optimal Solution Set ◽  
Hierarchical Genetic Algorithm

In this paper, we propose a genetic algorithm based design procedure for a multi-layer feed-forward neural network. A hierarchical genetic algorithm is used to evolve both the neural network's topology and weighting parameters. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies, including a feasibility check highlighted in literature. A multi-objective cost function is used herein to optimize the performance and topology of the evolved neural network simultaneously. In the prediction of Mackey–Glass chaotic time series, the networks designed by the proposed approach prove to be competitive, or even superior, to traditional learning algorithms for the multi-layer Perceptron networks and radial-basis function networks. Based upon the chosen cost function, a linear weight combination decision-making approach has been applied to derive an approximated Pareto-optimal solution set. Therefore, designing a set of neural networks can be considered as solving a two-objective optimization problem.

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