Employing Neural Networks for Manipulability Optimization of the Dual-Arm Cam-Lock Robot

2010 ◽  
Author(s):  
B. R. Jouibary ◽  
K. G. Osgouie ◽  
A. Meghdari
Keyword(s):  
Neural Networks ◽  
Degrees Of Freedom ◽  
Robot Manipulators ◽  
Optimal Configuration ◽  
Computational Time ◽  
Optimum Configuration ◽  
The Neural Network ◽  
Geometrical Constraints ◽  
The Cost ◽  
Dual Arm

Cooperative systems have been extensively investigated in literature. Herein the criteria and implementation for employing neural networks artificial intelligence for finding the optimal configuration of the Dual-Arm Cam-Lock (DACL) robot manipulators at a specific point with the objective to optimize the applicable task-space force in a desired direction are addressed. The DACL robot manipulators are reconfigurable arms formed by two parallel cooperative manipulators which operate redundantly but they may lock into each other in specific joints to increase structural stiffness in the cost of losing some degrees of freedom. Obtaining the optimal configuration demands lots of computational time and is not practical in real-time applications. The neural network is trained to approximate the optimum configuration considering the geometrical constraints of the planar arms using genetic algorithm as a multi-objective optimizer.

Optimal configuration of dual-arm cam-lock robot based on task-space manipulability

Robotica ◽  
2009 ◽  
Vol 27 (1) ◽  
pp. 13-18 ◽  
Author(s):  
Kambiz Ghaemi Osgouie ◽  
Ali Meghdari ◽  
Saeed Sohrabpour
Keyword(s):  
Degrees Of Freedom ◽  
Optimal Configuration ◽  
Task Space ◽  
Specific Point ◽  
Optimum Configuration ◽  
Geometrical Constraints ◽  
Dual Arm

SUMMARYIn this paper obtaining the optimal configuration of the dual-arm cam-lock (DACL) robot at a specific point is addressed. The objective is to optimize the applicable task-space force in a desired direction. The DACL robot is a reconfigurable manipulator formed by two parallel cooperative arms. The arms normally operate redundantly but when needed, they can lock into each other in certain joints in order to achieve a higher stiffness, while losing some degrees of freedom. Furthermore, the dynamics of the DACL robot is discussed and parametrically formulated. Considering the geometrical constraints at a given point in the robot's workspace, the optimum configuration for maximizing the cooperatively applicable force by dual arms is determined.

Optimal Configuration of a 4-DOF Dual-Arm Cam-Lock Manipulator

2007 ◽  
Author(s):  
Kambiz Ghaemi Osgouie ◽  
Ali Meghdari ◽  
Saeed Sohrabpour ◽  
Mehdi Salmani Jelodar
Keyword(s):  
Degrees Of Freedom ◽  
Robot Manipulators ◽  
The Other ◽  
Initial Population ◽  
Structural Stiffness ◽  
Task Space ◽  
Specific Point ◽  
Geometrical Constraints ◽  
Dual Arm ◽  
Cooperative Manipulators

The Dual-Arm Cam-Lock (DACL) robot manipulators are reconfigurable arms formed by two parallel cooperative manipulators. Some of their joints may lock into each other. Therefore, the arms normally operate redundantly. However, when higher structural stiffness is needed these two arms can lock into each other in specific joints and loose some degrees of freedom. In this paper, the dynamics of the DACL robot is discussed and parametrically formulated. On the other hand, the criteria and implementation of genetic algorithm (GA) to optimize the configuration of DACL robot manipulators at a specific point with the objective to maximize the cooperatively applicable task-space force in a desired direction are addressed. To obtain a more efficient process, an initial population is generated satisfying the geometrical constraints of the planar arms.

CHARACTERIZING ONE-LAYER ASSOCIATIVE NEURAL NETWORKS WITH OPTIMAL NOISE-REDUCTION ABILITY

1992 ◽  
Vol 06 (05) ◽  
pp. 1009-1025 ◽  
Author(s):  
TAO WANG ◽  
XIAOLIANG XING ◽  
XINHUA ZHUANG
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cost Function ◽  
Noise Reduction ◽  
Gradient Descent ◽  
Storage Capacity ◽  
Learning Algorithm ◽  
Optimal Learning ◽  
The Neural Network ◽  
The Cost

In this paper, we describe an optimal learning algorithm for designing one-layer neural networks by means of global minimization. Taking the properties of a well-defined neural network into account, we derive a cost function to measure the goodness of the network quantitatively. The connection weights are determined by the gradient descent rule to minimize the cost function. The optimal learning algorithm is formed as either the unconstraint-based or the constraint-based minimization problem. It ensures the realization of each desired associative mapping with the best noise reduction ability in the sense of optimization. We also investigate the storage capacity of the neural network, the degree of noise reduction for a desired associative mapping, and the convergence of the learning algorithm in an analytic way. Finally, a large number of computer experimental results are presented.

scholarly journals Long-Range Dependent Traffic Classification with Convolutional Neural Networks Based on Hurst Exponent Analysis

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1159
Author(s):  
Katarzyna Filus ◽  
Adam Domański ◽  
Joanna Domańska ◽  
Dariusz Marek ◽  
Jakub Szyguła
Keyword(s):  
Neural Networks ◽  
Hurst Exponent ◽  
Pareto Distribution ◽  
Synthetic Data ◽  
Traffic Classification ◽  
Traffic Data ◽  
Self Similarity ◽  
The Neural Network ◽  
The Cost ◽  
Real Traffic

The paper examines the ability of neural networks to classify Internet traffic data in terms of self-similarity expressed by the Hurst exponent. Fractional Gaussian noise is used for the generation of synthetic data for modeling the genuine ones. It is presented that the trained model is capable of classifying the synthetic data obtained from the Pareto distribution and the real traffic data. We present the results of training for different optimizers of the cost function and a different number of convolutional layers in the neural network.

Neural Network-Based Transfer Learning of Manipulator Inverse Displacement Analysis

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Houcheng Tang ◽  
Leila Notash
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Transfer Learning ◽  
Robot Manipulators ◽  
Learning Approach ◽  
Displacement Analysis ◽  
Performance Targets ◽  
The Neural Network ◽  
The Neural Networks ◽  
And Performance

Abstract In this paper, the feasibility of applying transfer learning for modeling robot manipulators is examined. A neural network-based transfer learning approach of inverse displacement analysis of robot manipulators is studied. Neural networks with different structures are applied utilizing data from different configurations of a manipulator for training purposes. Then, the transfer learning was conducted between manipulators with different geometric layouts. The training is performed on both the neural networks with pretrained initial parameters and the neural networks with random initialization. To investigate the rate of convergence of data fitting comprehensively, different values of performance targets are defined. The computing epochs and performance measures are compared. It is presented that, depending on the structure of the neural network, the proposed transfer learning can accelerate the training process and achieve higher accuracy. For different datasets, the transfer learning approach improves their performance differently.

Discrete Ordinate Radiative Transfer Model With the Neural Network Based Eigenvalue Solver: proof Of Concept

2021 ◽  
pp. 56-62
Author(s):  
Dmitry S. Efremenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Computational Time ◽  
Transfer Model ◽  
Discrete Ordinate ◽  
The Neural Network ◽  
Computationally Expensive ◽  
Eigenvalue Solver

Artificial neural networks are attracting increasing attention in various applications. They can be used as ‘universal approximations’, which substitute computationally expensive algorithms by relatively simple sequences of functions, which simulate a reaction of a set of neurons to the incoming signal. In particular, neural networks have proved to be efficient for parameterization of the computationally expensive radiative transfer models (RTMs) in atmospheric remote sensing. Although a direct substitution of RTMs by neural networks can lead to the multiple performance enhancements, such an approach has certain drawbacks, such as loss of generality, robustness issues, etc. In this regard, the neural network is usually trained for a specific application, predefined atmospheric scenarios and a given spectrometer. In this paper a new concept of neural-network based RTMs is examined, in which the neural network substitutes not the whole RTM but rather a part of it (the eigenvalue solver), thereby reducing the computational time while maintaining its generality. The explicit dependencies on geometry of observation and optical thickness of the medium are excluded from training. It is shown that although the speedup factor due to this approach is modest (around 3 times against 103 speed up factor of other approaches reported in recent papers), the resulting neural network is flexible and easy to train. It can be used for arbitrary number of atmospheric layers. Moreover, this approach can be used in conjunction with any RTMs based on the discrete ordinate method. The neural network is applied for simulations of the radiances at the top of the atmosphere in the Huggins band.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

scholarly journals Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

scholarly journals Sensuator: A Hybrid Sensor–Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 30
Author(s):  
Pornthep Preechayasomboon ◽  
Eric Rombokas
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Models ◽  
Open Loop ◽  
Proof Of Concept ◽  
State Estimator ◽  
Loop Control ◽  
Practical Applications ◽  
Soft Actuator ◽  
The Cost

Soft robotic actuators are now being used in practical applications; however, they are often limited to open-loop control that relies on the inherent compliance of the actuator. Achieving human-like manipulation and grasping with soft robotic actuators requires at least some form of sensing, which often comes at the cost of complex fabrication and purposefully built sensor structures. In this paper, we utilize the actuating fluid itself as a sensing medium to achieve high-fidelity proprioception in a soft actuator. As our sensors are somewhat unstructured, their readings are difficult to interpret using linear models. We therefore present a proof of concept of a method for deriving the pose of the soft actuator using recurrent neural networks. We present the experimental setup and our learned state estimator to show that our method is viable for achieving proprioception and is also robust to common sensor failures.

scholarly journals Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi–Dirac Correction Functions

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 223
Author(s):  
Yen-Ling Tai ◽  
Shin-Jhe Huang ◽  
Chien-Chang Chen ◽  
Henry Horng-Shing Lu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Computational Complexity ◽  
High Performance ◽  
Low Cost ◽  
Structural Complexity ◽  
Correction Function ◽  
Computational Time ◽  
Learning Methods ◽  
Band Theory

Nowadays, deep learning methods with high structural complexity and flexibility inevitably lean on the computational capability of the hardware. A platform with high-performance GPUs and large amounts of memory could support neural networks having large numbers of layers and kernels. However, naively pursuing high-cost hardware would probably drag the technical development of deep learning methods. In the article, we thus establish a new preprocessing method to reduce the computational complexity of the neural networks. Inspired by the band theory of solids in physics, we map the image space into a noninteraction physical system isomorphically and then treat image voxels as particle-like clusters. Then, we reconstruct the Fermi–Dirac distribution to be a correction function for the normalization of the voxel intensity and as a filter of insignificant cluster components. The filtered clusters at the circumstance can delineate the morphological heterogeneity of the image voxels. We used the BraTS 2019 datasets and the dimensional fusion U-net for the algorithmic validation, and the proposed Fermi–Dirac correction function exhibited comparable performance to other employed preprocessing methods. By comparing to the conventional z-score normalization function and the Gamma correction function, the proposed algorithm can save at least 38% of computational time cost under a low-cost hardware architecture. Even though the correction function of global histogram equalization has the lowest computational time among the employed correction functions, the proposed Fermi–Dirac correction function exhibits better capabilities of image augmentation and segmentation.

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