Modeling of Cell Deformation Under External Force Using Neural Network

2010 ◽  
Author(s):  
M. T. Ahmadian ◽  
G. R. Vossoughi ◽  
A. A. Abbasi ◽  
P. Raeissi
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Three Dimensional ◽  
Point Load ◽  
Cell Deformation ◽  
Back Propagation Algorithm ◽  
Three Dimensional Model ◽  
Error Back Propagation ◽  
Injection Process ◽  
The Neural Network

Embryogenesis, regeneration and cell differentiation in microbiological entities are influenced by mechanical forces. Therefore, development of mechanical properties of these materials is important. Neural network technique is a useful method which can be used to obtain cell deformation by the means of force-geometric deformation data or vice versa. Prior to insertion in the needle injection process, deformation and geometry of cell under external point-load is a key element to understand the interaction between cell and needle. In this paper the goal is the prediction of cell membrane deformation under a certain force, and to visually estimate the force of indentation on the membrane from membrane geometries. The neural network input and output parameters are associated to a three dimensional model without the assumption of the adherent affects. The neural network is modeled by applying error back propagation algorithm. In order to validate the strength of the developed neural network model, the results are compared with the experimental data on mouse oocyte and mouse embryos that are captured from literature. The results of the modeling match nicely the experimental findings.

Application of Neural Network for the Prediction of Tensile Properties of Friction Stir Welded Composites

2016 ◽  
Vol 880 ◽  
pp. 128-131 ◽  
Author(s):  
Arun Kumar Shettigar ◽  
Subramanya Prabhu ◽  
Rashmi Malghan ◽  
Shrikantha Rao ◽  
Mervin Herbert
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Base Material ◽  
Welding Process ◽  
Weld Strength ◽  
Back Propagation Algorithm ◽  
Friction Stir ◽  
Error Back Propagation ◽  
The Neural Network ◽  
Tool Pin

In this paper, an attempt has been made to apply the neural network (NN) techniques to predict the mechanical properties of friction stir welded composite materials. Nowadays, friction stri welding of composites are predominatally used in aerospace, automobile and shipbuilding applications. The welding process parameters like rotational speed, welding speed, tool pin profile and type of material play a foremost role in determining the weld strength of the base material. An error back propagation algorithm based model is developed to map the input and output relation of friction stir welded composite material. The proposed model is able to predict the joint strength with minimum error.

Neural network-based hardware classifier using CORDIC algorithm

2020 ◽  
Vol 34 (15) ◽  
pp. 2050161
Author(s):  
Vipin Tiwari ◽  
Ashish Mishra
Keyword(s):  
Neural Network ◽  
Programming Languages ◽  
Back Propagation ◽  
Activation Function ◽  
Data Repository ◽  
Cordic Algorithm ◽  
Back Propagation Algorithm ◽  
Error Back Propagation ◽  
Benchmark Datasets ◽  
Error Back Propagation Algorithm

This paper designs a novel classification hardware framework based on neural network (NN). It utilizes COordinate Rotation DIgital Computer (CORDIC) algorithm to implement the activation function of NNs. The training was performed through software using an error back-propagation algorithm (EBPA) implemented in C++, then the final weights were loaded to the implemented hardware framework to perform classification. The hardware framework is developed in Xilinx 9.2i environment using VHDL as programming languages. Classification tests are performed on benchmark datasets obtained from UCI machine learning data repository. The results are compared with competitive classification approaches by considering the same datasets. Extensive analysis reveals that the proposed hardware framework provides more efficient results as compared to the existing classifiers.

Manipulator Inverse Kinematics using an Adaptive Back-propagation Algorithm and Radial Basis Function with a Lookup Table

Robotica ◽  
1998 ◽  
Vol 16 (4) ◽  
pp. 433-444 ◽  
Author(s):  
A. S. Morris ◽  
M. A. Mansor
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Inverse Kinematics ◽  
Back Propagation ◽  
Lookup Table ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
The Neural Network ◽  
Radial Basis

This is an extension of previous work which used an artificial neural network with a back-propagation algorithm and a lookup table to find the inverse kinematics for a manipulator arm moving along pre-defined trajectories. The work now described shows that the performance of this technique can be improved if the back-propagation is made to be adaptive. Also, further improvement is obtained by using the whole workspace to train the neural network rather than just a pre-defined path. For the inverse kinematics of the whole workspace, a comparison has also been done between the adaptive back-propagation algorithm and radial basis function.

scholarly journals Building robust neural networks using different loss functions

2021 ◽  
pp. 67-82
Author(s):  
Maria Sivak ◽  
◽  
Vladimir Timofeev ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Loss Function ◽  
Back Propagation ◽  
Loss Functions ◽  
Back Propagation Algorithm ◽  
Error Back Propagation ◽  
Propagation Algorithm ◽  
Error Back Propagation Algorithm ◽  
Parameter Values

The paper considers the problem of building robust neural networks using different robust loss functions. Applying such neural networks is reasonably when working with noisy data, and it can serve as an alternative to data preprocessing and to making neural network architecture more complex. In order to work adequately, the error back-propagation algorithm requires a loss function to be continuously or two-times differentiable. According to this requirement, two five robust loss functions were chosen (Andrews, Welsch, Huber, Ramsey and Fair). Using the above-mentioned functions in the error back-propagation algorithm instead of the quadratic one allows obtaining an entirely new class of neural networks. For investigating the properties of the built networks a number of computational experiments were carried out. Different values of outliers’ fraction and various numbers of epochs were considered. The first step included adjusting the obtained neural networks, which lead to choosing such values of internal loss function parameters that resulted in achieving the highest accuracy of a neural network. To determine the ranges of parameter values, a preliminary study was pursued. The results of the first stage allowed giving recommendations on choosing the best parameter values for each of the loss functions under study. The second stage dealt with comparing the investigated robust networks with each other and with the classical one. The analysis of the results shows that using the robust technique leads to a significant increase in neural network accuracy and in a learning rate.

Application of neural networks in industrial production

2021 ◽  
pp. 10-17
Author(s):  
S. S. Yudachev ◽  
N. A. Gordienko ◽  
F. M. Bosy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Effective Means ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Graphics Processors ◽  
Training Scheme ◽  
Network Training ◽  
The Neural Network

The article describes an algorithm for the synthesis of neural networks for controlling the gyrostabilizer. The neural network acts as an observer of the state vector. The role of such an observer is to provide feedback to the gyrostabilizer, which is illustrated in the article. Gyrostabilizer is a gyroscopic device designed to stabilize individual objects or devices, as well as to determine the angular deviations of objects. Gyrostabilizer systems will be more widely used, as they provide an effective means of motion control with a number of significant advantages for various designs. The article deals in detail with the issue of specific stage features of classical algorithms: selecting the network architecture, training the neural network, and verifying the results of feedback control. In recent years, neural networks have become an increasingly powerful tool in scientific computing. The universal approximation theorem states that a neural network can be constructed to approximate any given continuous function with the required accuracy. The back propagation algorithm also allows effectively optimizing the parameters when training a neural network. Due to the use of graphics processors, it is possible to perform efficient calculations for scientific and engineering tasks. The article presents the optimal configuration of the neural network, such as the depth of memory, the number of layers and neurons in these layers, as well as the functions of the activation layer. In addition, it provides data on dynamic systems to improve neural network training. An optimal training scheme is also provided.

Research on Classification of Gait Signal Based on Neural Network

2013 ◽  
Vol 303-306 ◽  
pp. 1081-1084
Author(s):  
Jing Yin
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Recognition Rate ◽  
Back Propagation ◽  
Healthy People ◽  
Signal Recognition ◽  
Characteristic Parameters ◽  
Error Back Propagation ◽  
The Neural Network

To effectively recognize gait signal between healthy people and patients with Parkinson, a gait signal recognition model is established based on neural network of error back propagation (EBP), and a method is proposed to effectively extract characteristic parameters. In this paper, coefficient of variation is applied in the research of gait-pressure multi-characteristic parameters through gait-pressure signal, and the neural network model can automatically recognize gait-pressure characteristics between healthy people and patients with Parkinson. This can contribute to the recognition and diagnosis of patients with Parkinson. Experiment results show a recognition rate of 90%.

Flow Stress Prediction of High-Nb TiAl Alloys under High Temperature Deformation

2012 ◽  
Vol 510 ◽  
pp. 723-728 ◽  
Author(s):  
Liang Cheng ◽  
Hui Chang ◽  
Bin Tang ◽  
Hong Chao Kou ◽  
Jin Shan Li
Keyword(s):  
Neural Network ◽  
High Temperature ◽  
Flow Stress ◽  
Back Propagation ◽  
Three Dimensional ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Ann Model ◽  
The Neural Network ◽  
Hidden Layer

In this work, a back propagation artificial neural network (BP-ANN) model is conducted to predict the flow behaviors of high-Nb TiAl (TNB) alloys during high temperature deformation. The inputs of the neural network are deformation temperature, log strain rate and strain whereas flow stress is the output. There is a single hidden layer with 7 neutrons in the network, and the weights and bias of the network were optimized by Genetic Algorithm (GA). The comparison result suggests a very good correlation between experimental and predicted data. Besides, the non-experimental flow stress predicted by the network is shown to be in good agreement with the results calculated by three dimensional interpolation, which confirmed a good generalization capability of the proposed network.

scholarly journals Artificial Neural Network Approach for Solving Fuzzy Fractional order initial value problems under H-differentiability

2020 ◽  
Author(s):  
Somayeh Ezadi ◽  
Tofigh Allahviranloo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Initial Conditions ◽  
Error Function ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Neural Network Approach ◽  
Error Back Propagation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper aims to solve the celebrated Fuzzy Fractional Differential Equations (FFDE) using an Artificial Neural Network (ANN) technique. Compared to the integer order differential equation, the proposed FFDE can better describe several real application problems of various physical systems. To accomplish the aforementioned aim, the error back propagation algorithm and a multi-layer feed forward neural architecture are utilized using the unsupervised learning in order to minimize the error function as well as the modification of the parameters such as weights and biases. By combining the initial conditions with the ANN, output provides an appropriate approximate solution of the proposed FFDE. Then, two illustrative examples are solved to confirm the applicability of the concept as well as to demonstrate both the precision and effectiveness of the developed method. By comparing with some traditional methods, the obtained results reveals a close match that confirms both accuracy and correctness of the proposed method.

scholarly journals A Modified Neural Network system based on Morphological operations for detection of images with variation in Gray level intensity and facial expressions

2012 ◽  
pp. 56-61
Author(s):  
Pratibha Rani ◽  
Anshu Sirohi ◽  
Manish Kumar Singh
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Network System ◽  
Morphological Operations ◽  
Gray Level ◽  
Back Propagation Algorithm ◽  
Translation Invariant ◽  
The Neural Network ◽  
Neural Network System ◽  
Level Shifts

We introduce an algorithm based on the morphological shared-weight neural network. Which extract the features and then classify them. This type of network can work effectively, even if the gray level intensity and facial expression of the images are varied. The images are processed by a morphological shared weight neural network to detect and extract the features of face images. For the detection of the edges of the image we are using sobel operator. We are using back propagation algorithm for the purpose of learning and training of the neural network system. Being nonlinear and translation-invariant, the morphological operations can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The recognition efficiency of this modified network is about 98%.

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