PID control of an omnidirectional mobile platform based on an RBF neural network controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Niu Zijie ◽  
Zhang Peng ◽  
Yongjie Cui ◽  
Zhang Jun
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Control Algorithm ◽  
Rbf Neural Network ◽  
Mobile Platform ◽  
Content Type ◽  
The Neural Network ◽  
Omnidirectional Mobile Platform

Purpose Omnidirectional mobile platforms are still plagued by the problem of heading deviation. In four-Mecanum-wheel systems, this problem arises from the phenomena of dynamic imbalance and slip of the Mecanum wheels while driving. The purpose of this paper is to analyze the mechanism of omnidirectional motion using Mecanum wheels, with the aim of enhancing the heading precision. A proportional-integral-derivative (PID) setting control algorithm based on a radial basis function (RBF) neural network model is introduced. Design/methodology/approach In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. Findings The network RBF NN1 calculates the deviations ?Kp, ?Ki and ?Kd to regulate the three coefficients Kp, Ki and Kd of the heading angle PID controller. This corrects the driving heading in real time, resolving the problems of low heading precision and unstable driving. The experimental data indicate that, for a externally imposed deviation in the heading angle of between 34º and ∼38°, the correction time for an omnidirectional mobile platform applying the algorithm during longitudinal driving is reduced by 1.4 s compared with the traditional PID control algorithm, while the overshoot angle is reduced by 7.4°; for lateral driving, the correction time is reduced by 1.4 s and the overshoot angle is reduced by 4.2°. Originality/value In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. The method is innovative.

Using neural network model to estimate the rental price of residential properties

2019 ◽  
Vol 24 (2) ◽  
pp. 217-230
Author(s):  
Olalekan Shamsideen Oshodi ◽  
Wellington Didibhuku Thwala ◽  
Tawakalitu Bisola Odubiyi ◽  
Rotimi Boluwatife Abidoye ◽  
Clinton Ohis Aigbavboa
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Predictive Accuracy ◽  
Content Type ◽  
The Neural Network ◽  
Residential Properties ◽  
African Context ◽  
Neural Network Algorithm ◽  
The Government

Purpose Estimation of the rental price of a residential property is important to real estate investors, financial institutions, buyers and the government. These estimates provide information for assessing the economic viability and the tax accruable, respectively. The purpose of this study is to develop a neural network model for estimating the rental prices of residential properties in Cape Town, South Africa. Design/methodology/approach Data were collected on 14 property attributes and the rental prices were collected from relevant sources. The neural network algorithm was used for model estimation and validation. The data relating to 286 residential properties were collected in 2018. Findings The results show that the predictive accuracy of the developed neural network model is 78.95 per cent. Based on the sensitivity analysis of the model, it was revealed that balcony and floor area have the most significant impact on the rental price of residential properties. However, parking type and swimming pool had the least impact on rental price. Also, the availability of garden and proximity of police station had a low impact on rental price when compared to balcony. Practical implications In the light of these results, the developed neural network model could be used to estimate rental price for taxation. Also, the significant variables identified need to be included in the designs of new residential homes and this would ensure optimal returns to the investors. Originality/value A number of studies have shown that crime influences the value of residential properties. However, to the best of the authors’ knowledge, there is limited research investigating this relationship within the South African context.

scholarly journals Real-Time Extraction of Important Surgical Phases in Cataract Surgery Videos

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Shoji Morita ◽  
Hitoshi Tabuchi ◽  
Hiroki Masumoto ◽  
Tomofusa Yamauchi ◽  
Naotake Kamiura
Keyword(s):  
Neural Network ◽  
Cataract Surgery ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Correct Response ◽  
Response Rate ◽  
The Neural Network ◽  
End Times ◽  
Correct Response Rate

Abstract The present study aimed to conduct a real-time automatic analysis of two important surgical phases, which are continuous curvilinear capsulorrhexis (CCC), nuclear extraction, and three other surgical phases of cataract surgery using artificial intelligence technology. A total of 303 cases of cataract surgery registered in the clinical database of the Ophthalmology Department of Tsukazaki Hospital were used as a dataset. Surgical videos were downsampled to a resolution of 299 × 168 at 1 FPS to image each frame. Next, based on the start and end times of each surgical phase recorded by an ophthalmologist, the obtained images were labeled correctly. Using the data, a neural network model, known as InceptionV3, was developed to identify the given surgical phase for each image. Then, the obtained images were processed in chronological order using the neural network model, where the moving average of the output result of five consecutive images was derived. The class with the maximum output value was defined as the surgical phase. For each surgical phase, the time at which a phase was first identified was defined as the start time, and the time at which a phase was last identified was defined as the end time. The performance was evaluated by finding the mean absolute error between the start and end times of each important phase recorded by the ophthalmologist as well as the start and end times determined by the model. The correct response rate of the cataract surgical phase classification was 90.7% for CCC, 94.5% for nuclear extraction, and 97.9% for other phases, with a mean correct response rate of 96.5%. The errors between each phase’s start and end times recorded by the ophthalmologist and those determined by the neural network model were as follows: CCC’s start and end times, 3.34 seconds and 4.43 seconds, respectively and nuclear extraction’s start and end times, 7.21 seconds and 6.04 seconds, respectively, with a mean of 5.25 seconds. The neural network model used in this study was able to perform the classification of the surgical phase by only referring to the last 5 seconds of video images. Therefore, our method has performed like a real-time classification.

scholarly journals Summarize the Work Related to Prediction of Stock Market’s Option Prices with Artificial Intelligence using Standard Dataset

2019 ◽  
Vol 8 (4) ◽  
pp. 5023-5031
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Rbf Neural Network ◽  
Network Models ◽  
Energy Potential ◽  
Neural Network Models ◽  
The Neural Network ◽  
Bias Function

Forecasting and prediction are based on pattern recognition. It may be a human energy potential increase day today when he grownup a young guy, but afterward, his energy potential going downwards. So, we observed the pattern with the help of neural network models; these are radical bias function (RBP) and back-propagation (BP). Utilizing the neural network model, it also has many classification parts like a deep neural network, feedforward neural network, recurrent neural network, convolutional neural network and many more. In the forecasting or prediction, we have a large amount of data to manage. We trained the data with algorithm and here we also use the neural network models. We used optimization techniques that are inspired by biological swarm. Nowadays, lots of data generate day by day like market, medical, education, automobile, etc. we need recognition of the pattern for prediction of future expectations. That expectation of prediction very helpful and needy to gain profit of human beings. In this work, we use SOM (self-Organized Map), RBF (Radical Bias Function), DNN (Deep Neural Network) and PGO (Plant Grow Optimization). The total data point for the processing used 27500. The evaluation of the performance used standard parameters such as ET, MAE, MSE, RMSE and MI. The proposed algorithm implemented in MATLAB software. The cascaded neural network classifier is the combination of the SOM and RBF neural network models. The SOM neural network model proceeds the task of clustering and RBF neural network model used for prediction.

Understanding and predicting the quality determinants of e-government services

2015 ◽  
Vol 10 (3) ◽  
pp. 325-340 ◽  
Author(s):  
Sujeet Kumar Sharma ◽  
Srikrishna Madhumohan Govindaluri ◽  
Said Gattoufi
Keyword(s):  
Neural Network ◽  
Service Quality ◽  
Network Model ◽  
Neural Network Model ◽  
Network Models ◽  
Demographic Variables ◽  
Neural Network Models ◽  
Content Type ◽  
Government Services ◽  
The Neural Network

Purpose – The purpose of this paper is to investigate the quality determinants influencing the adoption of e-government services in Oman and compare the performance of multiple regression and neural network models in identifying the significant factors influencing adoption in Oman. Design/methodology/approach – Primary data concerning service quality determinants and demographic variables were collected using a structured questionnaire survey. The variables selected in the design of the questionnaire were based on an extensive literature review. Factor analysis, multiple linear regression and neural network models were employed to analyze data. Findings – The study found that quality determinants: responsiveness, security, efficiency and reliability are statistically significant predictors of adoption. The neural network model performed better than the regression model in the prediction of e-government services’ adoption and was able to characterize the non-linear relationship of the aforementioned predictors with the adoption of e-government services. Further, the neural network model was able to identify demographic variables as significant predictors. Practical implications – This study highlights the importance of service quality in the adoption of e-government services and suggests that an enhanced focus and investment on improving quality of the design and delivery of e-government services can have a positive impact on the usage of the services, thereby enabling the Oman Government in achieving the governance objectives for which these technologies were employed. Originality/value – Studies in the area of e-government typically focus either on technology adoption problems or service quality problems. The role of service quality in adoption is rarely addressed. The research presented in this paper is of great value to the institutions that are involved in the development of technology-based e-government services in Oman.

scholarly journals Setting Artificial Neural Network Hyperparameters for Mobile Platform Navigation

2020 ◽  
Vol 23 (6) ◽  
pp. 115-132
Author(s):  
D. M. Dudarenko ◽  
P. A. Smirnov
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Mobile Platform ◽  
Environmental Research ◽  
Target Point ◽  
Robotic Platform ◽  
Learning Time ◽  
The Neural Network ◽  
Optimized Model

Purpose of reseach. The main purpose of this work is to increase the efficiency of a neural network model when navigating a mobile robotic platform in static and dynamically generated environments.  Methods. To solve this problem, precise setting and optimization of neural network hyperparameters were proposed. In order to encourage agents to explore the environment, the reward system was adjusted to increase the reward when the distance from the agent to the target point was reduced, and the penalty increased when moving in the opposite direction to the end point and passing each subsequent scene. This distribution of rewards and penalties encourages agents to learn actively and helps to reduce the total number of scenes. In order to reduce the amount of data processed by a neural network, normalization of input vectors was introduced. The learning time of the neural network model was reduced due to the parallel training of agents and, consequently, increased experience as a result of the environmental research. Results. The proposed approach reduced the learning time by 30% and improved the navigation efficiency of the mobile platform by 10% in a dynamically generated environment and by 22% in a static environment compared to the non-optimized model. Conclusion. The proposed solution can be used in conjunction with other methods of tracing and navigation, when the taught neural network works simultaneously with the already developed and proven navigation algorithms, for example, if the mobile platform connects a taught neural network only to adjust the position in space and to prevent collisions with other objects. 

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

Assessment of the Underground Water Contaminated by the Leachate of Waste Dump of Open Pit Coal Mine Based on RBF Neural Network

2012 ◽  
Vol 599 ◽  
pp. 272-277 ◽  
Author(s):  
Zhi Bin Liu ◽  
Xiao Wei Yang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Coal Mine ◽  
Comprehensive Evaluation ◽  
Rbf Neural Network ◽  
Training Sample ◽  
Underground Water ◽  
Open Pit ◽  
Waste Dump

This paper used RBF artificial neural network to evaluate the underground water contaminated by the leachate of waste dump of open pit coal mine of Xinqiu in Fuxin. Firstly, with the advantages of neural network method in dealing with nonlinear problem, the RBF neural network model was built. Then, the normalized standard matrix was taken as training sample and the MATLAB software was used to train the training sample. Finally, the monitoring data were taken as test samples and were inputted in the RBF neural network model to evaluate the groundwater quality of study area. At the same time, the concept of degree of membership was adopted in the result making it more objective and accurate. The result shows that the ground water of this mining is seriously polluted, class of its pollution is Ⅳ-Ⅴ.The method with strong classification function and reliable evaluation results is simple and effective, and can be widely applied in all kinds of water resources comprehensive evaluation.

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