Spiking neural network-based target tracking control for autonomous mobile robots

Cao, Zhiqiang; Cheng, Long; Zhou, Chao; Gu, Nong; Wang, Xu; Tan, Min

doi:10.1007/s00521-015-1848-5

Cited by 49 publications

(24 citation statements)

References 34 publications

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“…Consequently, this article proposes Hybrid Neural primarily based on machine learning concept for rain prediction supported weather information consisting of options specifically vapor content, ratio, air pressure, and temperature. the information is gathered by dumdum bullet earth science center located at state [13,14,15,16,17].…”

Section: Jrefonaa M Lakshmi Allu Chaya Satya Kiran Anantha Ravimentioning

confidence: 99%

Rainfall Prediction using Apriori Algorithm

2019

ijrte

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The growth of machine learning techniques has paved way for predicting various areas and weather prediction is one of the most commonly used application. One of the most interesting part of research is rainfall prediction Rainfall prediction is done by various researchers as it is one of the prime aspects in detecting the rainfall and measuring its intensity beforehand to reduce the number of loss of lives and properties. Nowadays, severe rainfall is one of the major reason is most of disasters such as floods and landslides. Predicting its occurrence well before could be very much helpful in further disaster management operations. In this paper, we have predicted rainfall by making use of various machine learning techniques and is been deployed by using it in predicting Chennai rainfall dataset. The research work makes use of Apriori Algorithm to demonstrate the feature set and to look out for the possibility of rainfall. Preprocessing of the dataset is done in order to extract the features of the dataset and then the Apriori Algorithm is used. The designed model is proposed to produce about 95% of accuracy, potency and lesser time when compared to other model

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Section: Jrefonaa M Lakshmi Allu Chaya Satya Kiran Anantha Ravimentioning

confidence: 99%

Rainfall Prediction using Apriori Algorithm

2019

ijrte

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“…In recent years, a memorable attention in SNNs has grown and many types of researches studied SNNs as a controller. Cao et al (2015) [7] designed a three-layer SNN controller for target tracking of the autonomous robots. They used the SRM.…”

Section: Introductionmentioning

confidence: 99%

Design a Different Structures Controller for Controlled Systems Using a Spiking Neural Network

Hassan¹,

Mahmood²

2019

IJCCCE

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The design and simulation of the Spiking Neural Network (SNN) are proposed in this paper to control a plant without and with load. The proposed controller is performed using Spike Response Model. SNNs are more powerful than conventional artificial neural networks since they use fewer nodes to solve the same problem. The proposed controller is implemented using SNN to work with different structures as P, PI, PD or PID like to control linear and nonlinear models. This controller is designed in discrete form and has three inputs (error, integral of error and derivative of error) and has one output. The type of controller, number of hidden nodes, and number of synapses are set using external inputs. Sampling time is set according to the controlled model. Social-Spider Optimization algorithm is applied for learning the weights of the SNN layers. The proposed controller is tested with different linear and nonlinear models and different reference signals. Simulation results proved the efficiency of the suggested controller to reach accurate responses with minimum Mean Squared Error, small structure and minimum number of epochs under no load and load conditions.

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“…Recent advancement in the research of weather predictions have indicated that Artificial Neural Networks (ANNs or NNs) could be a suitable choice for predicting different weather parameters [4,10,11,14,28,29,38]. Further, studies have established that the NN based models are robust, accurate and prone to noisy data which is common in weather prediction models [3,8,12,19,31,37], [16]. Rainfall prediction has attracted the researchers and several successful models have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Neural Network Based Rainfall Prediction Supported by Flower Pollination Algorithm

Chatterjee¹,

Datta²,

Dey³

2018

NNW

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The present work proposes a hybrid neural network based model for rainfall prediction in the Southern part of the state West Bengal of India. The hybrid model is a multistep method. Initially, the data is clustered into a reasonable number of clusters by applying fuzzy c-means algorithm, then for every cluster a separate Neural Network (NN) is trained with the data points of that cluster using well known metaheuristic Flower Pollination Algorithm (FPA). In addition, as a preprocessing phase a feature selection phase is included. Greedy forward selection algorithm is employed to find the most suitable set of features for predicting rainfall. To establish the ingenuity of the proposed hybrid prediction model (Hybrid Neural Network or HNN) has been compared with two well-known models namely multilayer perceptron feed-forward network (MLP-FFN) using different performance metrics. The data set for simulating the model is collected from Dumdum meteorological station (West Bengal, India), recorded with in the 1989 to 1995. The simulation results have revealed that the proposed model is significantly better than traditional methods in predicting rainfall.

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Spiking neural network-based target tracking control for autonomous mobile robots

Cited by 49 publications

References 34 publications

Rainfall Prediction using Apriori Algorithm

Rainfall Prediction using Apriori Algorithm

Design a Different Structures Controller for Controlled Systems Using a Spiking Neural Network

Hybrid Neural Network Based Rainfall Prediction Supported by Flower Pollination Algorithm

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