scholarly journals Spiking PID Control Applied in the Van de Vusse Reaction

2021 ◽  
Vol 1 (4) ◽  
pp. 488-500
Author(s):  
Carlos Antonio Márquez-Vera ◽  
Zaineb Yakoub ◽  
Marco Antonio Márquez Vera ◽  
Alfian Ma'arif
Keyword(s):  
Neural Networks ◽  
Chemical Reaction ◽  
Pid Control ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Spiking Neuron ◽  
Control Applications ◽  
Real Behavior ◽  
Simulation Results ◽  
Inverse Response

Artificial neural networks (ANN) can approximate signals and give interesting results in pattern recognition; some works use neural networks for control applications. However, biological neurons do not generate similar signals to the obtained by ANN.  The spiking neurons are an interesting topic since they simulate the real behavior depicted by biological neurons. This paper employed a spiking neuron to compute a PID control, which is further applied to the Van de Vusse reaction. This reaction, as the inverse pendulum, is a benchmark used to work with systems that has inverse response producing the output to undershoot. One problem is how to code information that the neuron can interpret and decode the peak generated by the neuron to interpret the neuron's behavior. In this work, a spiking neuron is used to compute a PID control by coding in time the peaks generated by the neuron. The neuron has as synaptic weights the PID gains, and the peak observed in the axon is the coded control signal. The neuron adaptation tries to obtain the necessary weights to generate the peak instant necessary to control the chemical reaction. The simulation results show the possibility of using this kind of neuron for control issues and the possibility of using a spiking neural network to overcome the undershoot obtained due to the inverse response of the chemical reaction.

Quantized state simulation of spiking neural networks

SIMULATION ◽  
2011 ◽  
Vol 88 (3) ◽  
pp. 299-313 ◽  
Author(s):  
Guillermo L Grinblat ◽  
Hernán Ahumada ◽  
Ernesto Kofman
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Discrete Event ◽  
State System ◽  
Spiking Neurons ◽  
Spiking Neural Networks ◽  
Computational Costs ◽  
Simulation Results ◽  
Neuroscience Community

In this work, we explore the usage of quantized state system (QSS) methods in the simulation of networks of spiking neurons. We compare the simulation results obtained by these discrete-event algorithms with the results of the discrete time methods in use by the neuroscience community. We found that the computational costs of the QSS methods grow almost linearly with the size of the network, while they grows at least quadratically in the discrete time algorithms. We show that this advantage is mainly due to the fact that QSS methods only perform calculations in the components of the system that experience activity.

scholarly journals Integrating Non-Spiking Interneurons in Spiking Neural Networks

2020 ◽  
Author(s):  
Beck Strohmer ◽  
Rasmus Karnøe Stagsted ◽  
Poramate Manoonpong ◽  
Leon Bonde Larsen
Keyword(s):  
Neural Networks ◽  
Closed Loop ◽  
Spiking Neurons ◽  
Legged Robots ◽  
Spiking Neuron ◽  
Mixed Network ◽  
Spiking Neuron Model ◽  
Network Output ◽  
Mixed Networks ◽  
Event Based

AbstractResearchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well researched biological example of such a mixed network is the sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This pathway is also well researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network. Additionally, we show that non-spiking neurons can effectively manipulate post-synaptic spiking neurons in an event-based architecture. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.

Self-Tuning Neuro-PID for Stabilization of Double Inverted Pendulum

1998 ◽  
Vol 10 (5) ◽  
pp. 439-444
Author(s):  
Yoshiyuki Kishida ◽  
◽  
Sigeru Omatu ◽  
Michifumi Yoshioka
Keyword(s):  
Neural Networks ◽  
Pid Control ◽  
Pid Controller ◽  
Inverted Pendulum ◽  
Control Architecture ◽  
Self Tuning ◽  
Single Input ◽  
Simulation Results

This paper covers a new self-tuning neuro-PID control architecture and its application to stabilization of single and double inverted pendulums. Single-Input multioutput controls the inverted pendulum using the PID controller. PID gains are tuned using two types of neural networks. Simulation results demonstrate the effectiveness of the proposed approach.

Neuroscience-Inspired Parameter Selection of Spiking Neuron Using Hodgkin Huxley Model

2021 ◽  
Vol 13 (2) ◽  
pp. 89-106
Author(s):  
Ruchi Holker ◽  
Seba Susan
Keyword(s):  
Neural Networks ◽  
Spiking Neurons ◽  
Spiking Neural Networks ◽  
Model Parameters ◽  
Spiking Neuron ◽  
Artificial Brain ◽  
Class 1 ◽  
Optimal Values ◽  
Neuron Simulator ◽  
Selection Of

Spiking neural networks (SNN) are currently being researched to design an artificial brain to teach it how to think, perform, and learn like a human brain. This paper focuses on exploring optimal values of parameters of biological spiking neurons for the Hodgkin Huxley (HH) model. The HH model exhibits maximum number of neurocomputational properties as compared to other spiking models, as per previous research. This paper investigates the HH model parameters of Class 1, Class 2, phasic spiking, and integrator neurocomputational properties. For the simulation of spiking neurons, the NEURON simulator is used since it is easy to understand and code.

scholarly journals Integrating Non-spiking Interneurons in Spiking Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Beck Strohmer ◽  
Rasmus Karnøe Stagsted ◽  
Poramate Manoonpong ◽  
Leon Bonde Larsen
Keyword(s):  
Neural Networks ◽  
Closed Loop ◽  
Spiking Neurons ◽  
Legged Robots ◽  
Spiking Neuron ◽  
Mixed Network ◽  
Spiking Neuron Model ◽  
Network Output ◽  
Mixed Networks ◽  
Event Based

Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well-researched biological example of such a mixed network is a sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This type of pathway is also well-researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network inspired by the internal feedback loops found in insects for posturing. Additionally, we show that non-spiking neurons can effectively manipulate post-synaptic spiking neurons in an event-based architecture. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.

scholarly journals Hybrid memristor-CMOS neurons for in situ learning in fully hardware memristive spiking neural networks

2020 ◽  
Author(s):  
Xumeng Zhang ◽  
Jian Lu ◽  
Rui Wang ◽  
Jinsong Wei ◽  
Tuo Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hebbian Learning ◽  
Spiking Neurons ◽  
Spiking Neural Networks ◽  
Spiking Neural Network ◽  
Neuromorphic Computing ◽  
Efficient Data ◽  
Neuron Function

Abstract Spiking neural network, consisting of spiking neurons and plastic synapses, is a promising but relatively underdeveloped neural network for neuromorphic computing. Inspired by the human brain, it provides a unique solution for highly efficient data processing. Recently, memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware, owing to the close resemblance between their device dynamics and the biological counterparts. However, the functionalities of memristor-based neurons are currently very limited, and a hardware demonstration of fully memristor-based spiking neural networks supporting in situ learning is very challenging. Here, a hybrid spiking neuron by combining the memristor with simple digital circuits is designed and implemented in hardware to enhance the neuron functions. The hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in situ tuning of the connected synaptic weights. Finally, a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time, with which in situ Hebbian learning is achieved. This work opens up a way towards the implementation of spiking neurons, supporting in situ learning for future neuromorphic computing systems.

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.

scholarly journals Cooperative Spectrum Sensing Based on Convolutional Neural Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4440
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Neural Networks ◽  
Spectrum Sensing ◽  
Convolutional Neural Networks ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Majority Voting ◽  
Signal To Noise ◽  
Hidden Terminal ◽  
Simulation Results ◽  
Sensing Performance

Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, convolutional neural networks (CNN) are considered to extract the features of the observed signal and, as a consequence, improve the sensing performance. More specifically, a novel two-dimensional dataset of the received signal is established and three classical CNN (LeNet, AlexNet and VGG-16)-based CSS schemes are trained and analyzed on the proposed dataset. In addition, sensing performance comparisons are made between the proposed CNN-based CSS schemes and the AND, OR, majority voting-based CSS schemes. The simulation results state that the sensing accuracy of the proposed schemes is greatly improved and the network depth helps with this.

The Research about the Wind Turbines Pitch Control

2013 ◽  
Vol 773 ◽  
pp. 87-90
Author(s):  
Chen Wang ◽  
De Zhou Meng ◽  
Xu Fang Bo
Keyword(s):  
Wind Power ◽  
Pid Control ◽  
Control Algorithm ◽  
Uneven Distribution ◽  
Pitch Control ◽  
Experimental Platform ◽  
Simulation Results ◽  
Pitch System ◽  
Fan Blade ◽  
Variable Pitch Control

Based on the background of wind power, considering the wind blade sweep area on the uneven distribution, this paper is using the PID control algorithm to control the pitch system. At the same time, this paper is using Siemens SCL to programming, simulating on the experimental platform. Simulation results show the validity of the theory and the feasibility of the system, realizing variable pitch control of fan blade.

Intelligent Information of TS Fuzzy PID Control System of BLDCM

2013 ◽  
Vol 846-847 ◽  
pp. 313-316 ◽  
Author(s):  
Xiao Yun Zhang
Keyword(s):  
Control System ◽  
Dynamic Model ◽  
Pid Control ◽  
Control Method ◽  
Design Method ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Control Precision ◽  
Simulation Results ◽  
Intelligent Information

This paper presented a new method based on the Fuzzy self - adaptive PID for BLDCM. This method overcomes some defects of the traditional PID control. Such as lower control precision and worse anti - jamming performance. It dynamic model of BLDCM was built, and then design method for TS fuzzy PID model is given, At last, it compared simulation results of PID control method with TS Fuzzy PID control method. The results show that the TS Fuzzy PID control method has more excellent dynamic antistatic performances, as well as anti-jamming performance. The experiment shows that TS fuzzy PID control has the stronger adaptability robustness and transplant.

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