RBF neural network based RFID indoor localization method using artificial immune system

Li, Zhonghua; He, Guannan; Li, Muyuan; Ma, Le; Chen, Qiaofang; Jin-qiu, Huang; Cao, Jianzhong; Feng, Shuo; Gao, Haijian; Wang, Shanjin

doi:10.1109/ccdc.2018.8407608

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…Radial basis function neural network (RBFNN) has been widely used in many fields due to its simpler network structure, faster learning speeds, and better approximation capabilities [1,2]. RBFNN is a feed-forward neural network, which was first utilized by Moody and Darken [3]; they confirmed that the RBFNN has faster learning speed than the multilayer perceptron neural network (MLP).…”

Section: Introductionmentioning

confidence: 99%

“…The value of the neurons moves from the input layer, and passes the hidden layer to the output layer. The basis of including three layers strives for minimizing classification and forecast errors in RBFNN [1]. The appropriate operation of RBFNN primarily relies on the adequate parameter choice of its basic functions.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm metaheuristics are global improvement techniques-a popularly used algorithm to seek the near-optimal solution for RBFNN [13,22]. Numerous, naturally inspired, and latterly developed optimization algorithms include artificial immune systems (AIS) [1], artificial bee colony (ABC) [23], particle swarm optimization [24], differential evolution (DE) [25], genetic algorithm [26], etc. Some of these algorithms verified their appropriateness to numerous problems of engineering optimization [27].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

Alzaeemi

Sathasivam

2020

Processes

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A radial basis function neural network-based 2-satisfiability reverse analysis (RBFNN-2SATRA) primarily depends on adequately obtaining the linear optimal output weights, alongside the lowest iteration error. This study aims to investigate the effectiveness, as well as the capability of the artificial immune system (AIS) algorithm in RBFNN-2SATRA. Moreover, it aims to improve the output linearity to obtain the optimal output weights. In this paper, the artificial immune system (AIS) algorithm will be introduced and implemented to enhance the effectiveness of the connection weights throughout the RBFNN-2SATRA training. To prove that the introduced method functions efficiently, five well-established datasets were solved. Moreover, the use of AIS for the RBFNN-2SATRA training is compared with the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms. In terms of measurements and accuracy, the simulation results showed that the proposed method outperformed in the terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). The introduced method outperformed the existing four algorithms in the aspect of robustness, accuracy, and sensitivity throughout the simulation process. Therefore, it has been proven that the proposed AIS algorithm effectively conformed to the RBFNN-2SATRA in relation to (or in terms of) the average value of training of RMSE rose up to 97.5%, SBC rose up to 99.9%, and CPU time by 99.8%. Moreover, the average value of testing in MAE was rose up to 78.5%, MAPE—rose up to 71.4%, and was capable of classifying a higher percentage (81.6%) of the test samples compared with the results for the GA, DE, PSO, and ABC algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

Alzaeemi

Sathasivam

2020

Processes

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“…The majority of works use wireless measurements as the input, while some use data from camera [12], LiDAR [13], inertial [14], and sound [15] sensors. For wireless measurements, RSS (e.g., RSS from WiFi [16], BLE [17], ZigBee [18], RFID [19], cellular [20], and photodiode [21]), RSS features (e.g., two-dimensional RSS map [22], differential RSS [23], and RSS statistics [24]), channel information (e.g., the channel state information (CSI) [25] and channel impulse response (CIR) [26]), and angle-of-arrival (AoA) [27] have been used. These measurements are used for various purposes (i.e., outputs) through ANN.…”

Section: Introductionmentioning

confidence: 99%

“…When investigated on the types of ANN used, CNN [21] is the most widely used. Meanwhile, other types such as RNN [25], RBF [23], and MLP [20] have been used in multiple works. There are also ANN types or algorithms such as GAN [36], CPN [39], ANFIS [18], GCC [29], and TDNN [44].…”

Section: Introductionmentioning

confidence: 99%

Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning

Гао

et al. 2019

Sensors

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Although wireless fingerprinting has been well researched and widely used for indoor localization, its performance is difficult to quantify. Therefore, when wireless fingerprinting solutions are used as location updates in multi-sensor integration, it is challenging to set their weight accurately. To alleviate this issue, this paper focuses on predicting wireless fingerprinting location uncertainty by given received signal strength (RSS) measurements through the use of machine learning (ML). Two ML methods are used, including an artificial neural network (ANN)-based approach and a Gaussian distribution (GD)-based method. The predicted location uncertainty is evaluated and further used to set the measurement noises in the dead-reckoning/wireless fingerprinting integrated localization extended Kalman filter (EKF). Indoor walking test results indicated the possibility of predicting the wireless fingerprinting uncertainty through ANN the effectiveness of setting measurement noises adaptively in the integrated localization EKF.

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Deep Convolutional Neural Network for Passive RFID Tag Localization Via Joint RSSI and PDOA Fingerprint Features

Peng

Jiang

2021

IEEE Access

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Radio-frequency identification (RFID) localization has drawn much attention with the emergence of the Internet of Things (IoT). Deep learning with applications to RFID localization owns a lot of advantages. In this paper, we present a deep convolutional neural network (CNN)-based approach for passive RFID tag localization exploiting joint fingerprint features of the received signal strength indication (RSSI) and phase difference of arrival (PDOA). First, the RSSI and PDOA data are extracted from the received signals of RFID readers. Then, a CNN with three convolution layers and pooling layers is design, in which the normalized RSSI and PDOA data are formed into images as its input to train the weights in the offline stage. In the online stage, the RSSI and PDOA data of test tags are collect and then the positions of unknown tags is predected based on the designed CNN. In the simulations, the accuracy of the proposed approach is compared with several fingerprinting-based schemes such as LANDMARC, weighted K-nearest neighbor (WKNN) and deep neural network (DNN), and the impact of different fingerprint data sets and noise variances on the positioning accuracy is analyzed. Experiments show that the proposed approach can locate multiple tags with high accuracy and stability in complex indoor environment, and outperforms other existing schemes.

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RBF neural network based RFID indoor localization method using artificial immune system

Cited by 8 publications

References 13 publications

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning

Deep Convolutional Neural Network for Passive RFID Tag Localization Via Joint RSSI and PDOA Fingerprint Features

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