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

2019 ◽  
pp. 18-29
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Nonlinear Models ◽  
Spiking Neural Network ◽  
Spike Response ◽  
Controlled Systems ◽  
Minimum Mean Squared Error ◽  
Minimum Number ◽  
Linear And Nonlinear ◽  
Spike Response Model

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.

scholarly journals Smartphone Sensor Accelerometer Data for Human Activity Recognition Using Spiking Neural Network

2021 ◽  
Vol 11 (4) ◽  
pp. 298-303
Author(s):  
Nor Surayahani Suriani ◽  
◽  
Fadilla ‘Atyka Nor Rashid
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Computational Cost ◽  
Human Activity Recognition ◽  
Spiking Neural Network ◽  
Accelerometer Data ◽  
Spike Response ◽  
Spike Response Model ◽  
Artificial Network

Recognizing human actions is a challenging task and actively research in computer vision community. The task of human activity recognition has been widely used in various application such as human monitoring in a hospital or public spaces. This work applied open dataset of smartphones accelerometer data for various type of activities. The analogue input data is encoded into the spike trains using some form of a rate-based method. Spiking neural network is a simplified form of dynamic artificial network. Therefore, this network is expected to model and generate action potential from the leaky integrate-and-fire spike response model. The leaning rule is adaptive and efficient to present synapse exciting and inhibiting firing neuron. The result found that the proposed model presents the state-of-the-art performance at a low computational cost.

scholarly journals Improved DV-Hop Localization Algorithm for Wireless Sensor Networks

2020 ◽  
Vol 9 (5) ◽  
pp. 1324-1330
Keyword(s):  
Mean Squared Error ◽  
Classical Method ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Minimum Number ◽  
Anchor Nodes ◽  
Error Method

Considering the emerging applications of Wireless Sensor Network, the localization of sensor nodes is very important in some applications. Hence in this paper, proposed an improved Distance-Vector hop technique to reduce the localization error generated in the classical method of range free DV-hop algorithm. The proposed algorithm targets to minimize the error introduced in the average hop-size value by the series of mathematical corrections like minimum mean squared error method, modifying the average hop-value value calculated between known sensor nodes with respect to error-term calculated by comparing the estimated distance with actual distance or Euclidean value of anchor nodes, by using dynamic coefficient of weight with respect to minimum number of hopes, calculating x and y co-ordinates of unknown by using 2-D-hyperbolic method. The final corrections on the x and y values are done by using some geometrical methods. Simulations and results are executed on three different evolution models of network by varying radius of communication range, anchor node percentage and number of sensor nodes

Synthesis of MnFe2O4 and Mn3O4 magnetic nano-composites with enhanced properties for adsorption of Cr(VI): artificial neural network modeling

2017 ◽  
Vol 76 (12) ◽  
pp. 3368-3378 ◽  
Author(s):  
Kartick Lal Bhowmik ◽  
Animesh Debnath ◽  
Ranendu Kumar Nath ◽  
Biswajit Saha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adsorption Capacity ◽  
Metal Ion ◽  
Mean Squared Error ◽  
Nano Particles ◽  
Neural Network Modeling ◽  
Potential Candidate ◽  
Minimum Mean Squared Error ◽  
Artificial Neural

Abstract This study reports adsorptive removal of Cr(VI) by magnetic manganese ferrite and manganese oxide nano-particles (MnF-MO-NPs) composite from aqueous media. The X-ray diffraction pattern of MnF-MO-NPs revealed a polycrystalline nature with nanoscale crystallite size. The prepared adsorbent with high Brunauer–Emmett–Teller specific surface area of 100.62 m2/g and saturation magnetization of 30.12 emu/g exhibited maximum Cr(VI) removal at solution pH 2.0 and was easily separated from water under an external magnetic field. Adsorption capacity as much as 91.24 mg/g is reported and electrostatic interaction between positively charged adsorbent surface and anionic metal ion species is the main driving force in this adsorption. Adsorption experimental data followed Langmuir isotherm and second order kinetics. Partial involvement of intra-particle diffusion was also observed due to the mesoporous nature of MnF-MO-NPs. The thermodynamic studies revealed that the process was favorable, spontaneous and exothermic in nature. An artificial neural network model was developed for accurate prediction of Cr(VI) ions removal with minimum mean squared error (MSE) of 15.4 × 10−4 and maximum R2 of 0.98. Owing to large surface to volume ratio, advantage of easy magnetic separation, and high adsorption capacity towards Cr(VI), the reported MnF-MO-NPs appear to be a potential candidate in Cr(VI) contaminated wastewater remediation.

A Comparison Study between Integrated OBFARX-NN and OBF-NN for Modeling of Nonlinear Systems in Extended Regions of Operation

2014 ◽  
Vol 625 ◽  
pp. 382-385
Author(s):  
Haslinda Zabiri ◽  
M. Ariff ◽  
Lemma Dendena Tufa ◽  
Marappagounder Ramasamy
Keyword(s):  
Neural Network ◽  
Nonlinear Models ◽  
Nonlinear System Identification ◽  
Model Performance ◽  
Identification Algorithm ◽  
Comparison Study ◽  
Auto Regressive ◽  
Linear And Nonlinear ◽  
Parallel Integration

In this paper the combination of linear and nonlinear models in parallel for nonlinear system identification is investigated. A residuals-based sequential identification algorithm using parallel integration of linear Orthornormal basis filters-Auto regressive with exogenous input (OBFARX) and a nonlinear neural network (NN) models is developed. The model performance is then compared against previously developed parallel OBF-NN model in a nonlinear CSTR case study in extended regions of operation (i.e. extrapolation capability).

Multi-AUV Aided Cooperative 3D-localization for Underwater Sensor Networks

2020 ◽  
Vol 13 (1) ◽  
pp. 80-90 ◽  
Author(s):  
Meiyan Zhang ◽  
Wenyu Cai
Keyword(s):  
Sensor Networks ◽  
Mean Squared Error ◽  
Autonomous Underwater Vehicles ◽  
Estimation Method ◽  
Location Estimation ◽  
Underwater Sensor Networks ◽  
Coverage Ratio ◽  
Minimum Mean Squared Error ◽  
3D Localization ◽  
Localization Scheme

Background: Effective 3D-localization in mobile underwater sensor networks is still an active research topic. Due to the sparse characteristic of underwater sensor networks, AUVs (Autonomous Underwater Vehicles) with precise positioning abilities will benefit cooperative localization. It has important significance to study accurate localization methods. Methods: In this paper, a cooperative and distributed 3D-localization algorithm for sparse underwater sensor networks is proposed. The proposed algorithm combines with the advantages of both recursive location estimation of reference nodes and the outstanding self-positioning ability of mobile AUV. Moreover, our design utilizes MMSE (Minimum Mean Squared Error) based recursive location estimation method in 2D horizontal plane projected from 3D region and then revises positions of un-localized sensor nodes through multiple measurements of Time of Arrival (ToA) with mobile AUVs. Results: Simulation results verify that the proposed cooperative 3D-localization scheme can improve performance in terms of localization coverage ratio, average localization error and localization confidence level. Conclusion: The research can improve localization accuracy and coverage ratio for whole underwater sensor networks.

scholarly journals Estimation of the influence of spiking neural network parameters on classification accuracy using a genetic algorithm

2018 ◽  
Vol 145 ◽  
pp. 488-494 ◽  
Author(s):  
Aleksandr Sboev ◽  
Alexey Serenko ◽  
Roman Rybka ◽  
Danila Vlasov ◽  
Andrey Filchenkov
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Spiking Neural Network ◽  
Network Parameters

scholarly journals Radar Emitter Identification Based on Fully Connected Spiking Neural Network

2021 ◽  
Vol 1914 (1) ◽  
pp. 012036
Author(s):  
LI Wei ◽  
Zhu Wei-gang ◽  
Pang Hong-feng ◽  
Zhao Hong-yu
Keyword(s):  
Neural Network ◽  
Spiking Neural Network ◽  
Fully Connected

scholarly journals Spatial Memory in a Spiking Neural Network with Robot Embodiment

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2678
Author(s):  
Sergey A. Lobov ◽  
Alexey I. Zharinov ◽  
Valeri A. Makarov ◽  
Victor B. Kazantsev
Keyword(s):  
Neural Network ◽  
Spatial Memory ◽  
Internal Representation ◽  
Learning Curves ◽  
Global Network ◽  
Spiking Neural Network ◽  
Interference Phenomenon ◽  
Brain Functioning ◽  
Information Characteristics ◽  
Catastrophic Interference

Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous zones. We propose a measure of the global network memory using the synaptic vector field approach to validate results and calculate information characteristics, including learning curves. We show that after training, the SNN can effectively control the robot’s cognitive behavior, allowing it to avoid dangerous regions in the arena. However, the learning is not perfect. The robot eventually visits dangerous areas. Such behavior, also observed in animals, enables relearning in time-evolving environments. If a dangerous zone moves into another place, the SNN remaps positive and negative areas, allowing escaping the catastrophic interference phenomenon known for some AI architectures. Thus, the robot adapts to changing world.

scholarly journals Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1065
Author(s):  
Moshe Bensimon ◽  
Shlomo Greenberg ◽  
Moshe Haiut
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Analog Circuits ◽  
Continuous Time ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Sound Classification ◽  
Classification Framework ◽  
Neural Network Structure ◽  
Simple Analog

This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.

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