Integrated neural network model with pre-RBF kernels

To improve the network performance of radial basis function (RBF) and back-propagation (BP) networks on complex nonlinear problems, an integrated neural network model with pre-RBF kernels is proposed. The proposed method is based on the framework of a single optimized BP network and an RBF network. By integrating and connecting the RBF kernel mapping layer and BP neural network, the local features of a sample set can be effectively extracted to improve separability; subsequently, the connected BP network can be used to perform learning and classification in the kernel space. Experiments on an artificial dataset and three benchmark datasets show that the proposed model combines the advantages of RBF and BP networks, as well as improves the performances of the two networks. Finally, the effectiveness of the proposed method is verified.

An Implementation of First and Second Order Neural Network Classification on Potential Drug Addict Repetition

Back propagation (BP) neural network is known for its popularity and its capability in prediction and classification. BP used gradient descent (GD) method as one of the most widely used error minimization methods used to train back propagation (BP) networks. Besides its popularity BP still faces some limitation such as very slow in learning as well as easily get stuck at local minima. Many techniques have been introduced to improve BP performance. This research implements second order method together with gradient descent in order to improve its performance. The efficiency of both methods are verified and compared by means of simulations on classifying drug addict repetition. The results show that the second order methods are more reliable and significantly improves the learning performance of BP.

PULSE ELECTRODEPOSITED NANO-SIZED Ni–AlN THIN FILMS: PREPARATION AND CORROSION RESISTANCE PREDICTION USING BACKWARD PROPAGATION NETWORK MODEL

Nano-sized Ni–AlN thin films were prepared by pulse electrodeposition (PE) on A3 steel substrates. The microstructures, surface root-mean-square roughnesses, microhardness values, and corrosion performances of obtained nano-sized Ni–AlN thin films were investigated. Corrosion weight losses of Ni–AlN thin films were predicted by Backward propagation (BP) networks model. The results indicated that average diameters of Ni and AlN embedded in the films were evaluated to 57.9 and 29.2[Formula: see text]nm, respectively. Also, thin films were observed with uniform and compact surface structures, and Rq value was estimated to only 32.75[Formula: see text]nm.

The Direct Kinematics Solution of the 6-RSPS Simulator Platform with BP Networks

A new structure of 6-RSPS simulator platform which can be rotated infinitely is presented. The solution of direct kinematics problem of parallel structure is the fundamental problem. For the solution of this problem, according to the links vector diagram, the relationship of the position and orientation of the platform between the length of links and the rotate angle of the under-hinge is derived. BP network is applied, then using the previous data obtained by inverse kinematics as the training data and testing data of BP network, the direct kinematics solution is obtained. Simulation results verified that the solution of direct kinematics applied the BP network can be meet the requirements completely and the error percentages are within the acceptable range.

Applied Technology on Artificial Neural Network in Fault Diagnosis System

This paper combined Rumelhart’s adding inertial impulse and dynamically adjusting the learning rate and proposed an improved algorithm to optimize the Back Propagation (BP) networks with applied technology. This improved BP networks is used to determining membership function and applied in fuzzy diagnosing vapor congealing equipment. The application results prove that the improved BP algorithm is effective and the convergence speed is accelerated and is much faster than the classic BP algorithm. The applied technology is very useful in the application course.