Conjugate Gradient Backpropagation Algorithm Research Articles

Modeling and optimization of electrospun PAN nanofiber diameter using response surface methodology and artificial neural networks

AbstractResponse surface methodology (RSM) based on a three‐level, three‐variable Box‐Benkhen design (BBD), and artificial neural network (ANN) techniques were compared for modeling the average diameter of electrospun polyacrylonitrile (PAN) nanofibers. The multilayer perceptron (MLP) neural networks were trained by the sets of input–output patterns using a scaled conjugate gradient backpropagation algorithm. The three important electrospinning factors were studied including polymer concentration (w/v%), applied voltage (kV) and the nozzle‐collector distance (cm). The predicted fiber diameters were in agreement with the experimental results in both ANN and RSM techniques. High‐regression coefficient between the variables and the response (R2 = 0.998) indicates excellent evaluation of experimental data by second‐order polynomial regression model. The R2 value was 0.990, which indicates that the ANN model was shows good fitting with experimental data. Moreover, the RSM model shows much lower absolute percentage error than the ANN model. Therefore, the obtained results indicate that the performance of RSM was better than ANN. The RSM model predicted the 118 nm value of the finest nanofiber diameter at conditions of 10 w/v% polymer concentration, 12 cm of nozzle‐collector distance, and 12 kV of the applied voltage. The predicted value (118 nm) showed only 2.5%, difference with experimental results in which 121 nm at the same setting were observed. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012

Patch Antenna Array Fault Modeling and Its Monitoring

The On/Off faults are among the most prominent hazards observed in antenna arrays. They behave like a catalyst towards steady degradation of the circuit's performance. This paper is regarding fault monitoring in the microstrip patch antenna array (1)(6). The fault modeling, included here is single and double fault models of the patch antenna array of 8 elements. There is a marked deviation in the parametric properties of the array because of On/Off faults. By observation and analysis of these trends, the detection of fault in the array is simplified to a great extent. We aim at identification of On/Off faults in a faulty array structure by observation of its characteristic properties (like S- parameters and Radiation Pattern) and their analysis using Matrix Comparison Method and Scaled Conjugate Gradient Back Propagation Algorithm in Artificial Neural Network.

Convergence Conditions for Ascent Methods

Convergence Conditions for Ascent Methods