A consecutive hybrid spiking-convolutional (CHSC) neural controller for sequential decision making in robots

Azimirad, Vahid; Ramezanlou, Mohammad Tayefe; Sotubadi, Saleh Valizadeh; Janabi–Sharifi, Farrokh

doi:10.1016/j.neucom.2021.11.097

Cited by 22 publications

(14 citation statements)

References 32 publications

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“…All considered machine learning mthods 72 – 74 have some parameters that need to be tuned using historical data of a given problem and an optimization algorithm 75 . This research utilizes 792 experimental data of H 2 S solubility in fifteen ILs versus pressure, temperature, acentric factor, critical pressure, and temperature.…”

Section: Resultsmentioning

confidence: 99%

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Abdi

Hadipoor

Esmaeili-Faraj

et al. 2022

Sci Rep

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Absorption has always been an attractive process for removing hydrogen sulfide (H2S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H2S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H2S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H2S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared. A vast experimental databank comprising 792 datasets was utilized. Temperature, pressure, acentric factor, critical pressure, and critical temperature of investigated ILs are the affecting parameters of our models. Sensitivity and statistical error analysis were utilized to assess the performance and accuracy of the proposed models. The calculated solubility data and the derived models were validated using seven statistical criteria. The obtained results showed that the LS-SVM accurately predicts H2S solubility in ILs and possesses R2, RMSE, MSE, RRSE, RAE, MAE, and AARD of 0.99798, 0.01079, 0.00012, 6.35%, 4.35%, 0.0060, and 4.03, respectively. It was found that the H2S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM+ and Tf2N-, i.e., [OMIM][Tf2N] ionic liquid, is the best choice for H2S capture among the investigated absorbents. The H2S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.

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Section: Resultsmentioning

confidence: 99%

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Abdi

Hadipoor

Esmaeili-Faraj

et al. 2022

Sci Rep

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show abstract

“…Developing trustworthy, robust, and precise topologies to correlate and model highly nonlinear concepts are an arduous, uphill, and time‐consuming task, which sometimes is not possible to achieve. The ANNs as nonlinear learning approaches were developed based on the biological nervous systems of the human brain have a great potential for data analysis, process design, fault detection, and algorithm assessment 54 . In this way, recently, ANNs have attracted significant attention in the areas where precise empirical or semi‐empirical correlations are not available as well as measuring experimental valuers in a broad range is an infeasible mission 55 .…”

Section: Methodsmentioning

confidence: 99%

“…The ANNs as nonlinear learning approaches were developed based on the biological nervous systems of the human brain have a great potential for data analysis, process design, fault detection, and algorithm assessment. 54 In this way, recently, ANNs have attracted significant attention in the areas where precise empirical or semi-empirical correlations are not available as well as measuring experimental valuers in a broad range is an infeasible mission. 55 Generally, creating robust ANN models does not require accurate relations among the input and output values or the parametric nature of considered variables.…”

Section: Annsmentioning

confidence: 99%

Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches

Karimi

Khosravi

Fathollahi

et al. 2022

Energy Science & Engineering

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Heat capacity is among the most well‐known thermal properties of cellulosic biomass samples. This study assembles a general machine learning model to estimate the heat capacity of the cellulosic biomass samples with different origins. Combining the uncertainty and ranking analyses over 819 artificial intelligence models from seven different categories confirmed that the least‐squares support vector regression (LSSVR) with the Gaussian kernel function is the best estimator. This model is validated using 700 laboratory heat capacities of four cellulosic biomass samples in wide temperature ranges (absolute average relative deviation = 0.32%, mean square errors = 1.88 × 10−3, and R2 = 0.999991). The data validity investigation approved that only one out of 700 experimental data is an outlier. The LSSVR model considers the effect of the cellulosic samples' crystallinity, temperature, and sulfur and ash content on their heat capacity. The overall prediction accuracy of the LSSVR is more than 62% better than the achieved accuracy using the empirical correlation.

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“…Higher cumulative rewards can be obtained with better-followed target velocities and lower muscle effort in this study's musculoskeletal model and physics-based simulation environment. A general RL problem involves receiving observations o t at timestep t and querying its policy for the action a t (excitation values of the muscles in the model) at timestep t. Observations are full or partial descriptions of the state of the environment at timestep t. π(a t |o t ) can be either stochastic or deterministic, with a stochastic policy defining a distribution over actions at timestep t [38][39][40]. It is possible to calculate gradients from non-differentiable objective functions [41], such as those generated from neuromechanical simulations, and then use the gradients as a basis for updating the policies.…”

Section: Reinforcement Learning For Simulation Of Human Locomotionmentioning

confidence: 99%

Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling

Korivand¹,

Jalili²,

Gong³

2022

Preprint

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Locomotor impairment is a high-prevalent and significant source of disability and significantly impacts a large population’s quality of life. Despite decades of research in human locomotion, the challenges of simulating human movement to study the features of musculoskeletal drivers and clinical conditions remain. Most recent efforts in utilizing reinforcement learning (RL) techniques are promising to simulate human locomotion and reveal musculoskeletal drives. However, these simulations often failed to mimic natural human locomotion because most reinforcement strategies have yet to consider any reference data regarding human movement. To address these challenges, in this study, we designed a reward function based on the trajectory optimization rewards (TOR), and bio-inspired rewards, which includes the rewards obtained from reference motion data captured by a single Intertial Moment Unit (IMU) sensor. The sensor was equipped on the participants’ pelvis to capture reference motion data. Also, we adapted the reward function by leveraging previous research in walking simulation for TOR. The experimental results showed that the simulated agents with the modified reward function performed better in mimicking the collected IMU data from participants, which means the simulated human locomotion was more realistic. Also, as this bio-inspired defined cost, IMU data enhanced the agent’s capacity to converge during the training process. As a result, the models’ convergence is faster than those developed without reference motion data. Consequently, human locomotion can be simulated more quicker and in a broader range of environments with a better simulation performance.

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A consecutive hybrid spiking-convolutional (CHSC) neural controller for sequential decision making in robots

Cited by 22 publications

References 32 publications

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches

Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling

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