Design of Legal Chinese English Simultaneous Interpretation System based on PSO Algorithm

Aiming at the phenomenon of “wrong words” and “missing words” in the process of Chinese English legal interpretation, a Chinese English legal simultaneous interpretation system based on PSO algorithm is designed. According to the construction requirements of fuzzy neural network, the optimization results of PSO inertia weight are determined, and then the system model optimization based on PSO algorithm is realized with the help of membership function. On this basis, this paper analyzes the key trigger factors of simultaneous interpretation, and distinguishes the specific differences between consecutive interpretation load and simultaneous interpretation by defining the way of legal Chinese English text transmission effect, so as to realize the smooth application of legal Chinese English simultaneous interpretation system based on PSO algorithm. The results shows that, compared with the consecutive interpretation system, the simultaneous interpretation system can effectively solve all the problems of “wrong words” and “missing words” in the process of legal Chinese English document translation, and effectively guarantee the authenticity of document samples.

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Applying Artificial Intelligence to Short-Term Traffic Flow Forecasting

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.5214 ◽

2012 ◽

Vol 433-440 ◽

pp. 5214-5217

Author(s):

Hai Huang

Keyword(s):

Neural Network ◽

Traffic Flow ◽

Optimization Algorithm ◽

Fuzzy Neural Network ◽

Pso Algorithm ◽

Local Optimum ◽

Short Term ◽

Traffic Flow Forecasting ◽

Fuzzy Neural ◽

Low Efficiency

Short-term traffic flow forecasting has a high requirement for the responding time and accuracy of the forecasting method because the result is directly used for instant traffic inducing. Based on the introduction of the fuzzy neural network model for short-term traffic flow forecasting together with its detailed procedures, this paper adopt the particle swarm optimization algorithm to train the fuzzy neural network. Its global searching and optimization algorithm helps to overcome the shortcomings of the traditional fuzzy neural network, such as its low efficiency and “local optimum”. A case study is also given for the PSO algorithm to train the fuzzy neural network for traffic flow forecasting. The result shows that the average square error is 0.932 when the PSO algorithm is put to use for the network training, which is 3.926 when the PSO is not used. Thus result is more accurate and it requires less time for the training procedures. It proves this method is feasible and efficient.

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Temperature Control in Brazing Furnace Based on B-Spline Fuzzy Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.712-715.2816 ◽

2013 ◽

Vol 712-715 ◽

pp. 2816-2820

Author(s):

Qiang Guo Yu

Keyword(s):

Neural Network ◽

Temperature Control ◽

Spline Function ◽

Fuzzy Neural Network ◽

Pso Algorithm ◽

Fuzzy Membership Function ◽

Swarm Optimization ◽

B Spline ◽

Fuzzy Neural ◽

And Training

According to the temperature control of brazing furnace, a fuzzy B-spline function neural network based on the Particle Swarm Optimization (PSO) algorithm is proposed by using B-spline function as fuzzy membership function and using neural network to realize fuzzy interference, using PSO to completes the network' s weights' learning and training. The result shows the system is validity and feasibility.

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Modified PSO Algorithm on Recurrent Fuzzy Neural Network for System Identification

Intelligent Automation & Soft Computing ◽

10.31209/2019.100000094 ◽

2019 ◽

pp. -1--1 ◽

Cited By ~ 1

Author(s):

Wei Lung Mao ◽

Chung Wen Hung ◽

Han Yi Huang

Keyword(s):

Neural Network ◽

System Identification ◽

Fuzzy Neural Network ◽

Pso Algorithm ◽

Fuzzy Neural ◽

Modified Pso ◽

Recurrent Fuzzy Neural Network

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A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

Matériaux & Techniques ◽

10.1051/mattech/2019002 ◽

2018 ◽

Vol 106 (6) ◽

pp. 603 ◽

Cited By ~ 2

Author(s):

Bendaoud Mebarek ◽

Mourad Keddam

Keyword(s):

Neural Network ◽

Experimental Data ◽

Fuzzy Neural Network ◽

Treatment Time ◽

Calculation Model ◽

Average Error ◽

Network Approach ◽

Neural Network Approach ◽

Fuzzy Neural ◽

Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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