An accelerated particle swarm optimization based levenberg marquardt back propagation algorithm

The Levenberg Marquardt (LM) algorithm is one of the most effective algorithms in speeding up the convergence rate of the Artificial Neural Networks (ANN) with Multilayer Perceptron (MLP) architectures. However, the LM algorithm suffers the problem of local minimum entrapment. Therefore, we introduce several improvements to the Levenberg Marquardt algorithm by training the ANNs with meta-heuristic nature inspired algorithm. This paper proposes a hybrid technique Accelerated Particle Swarm Optimization using Levenberg Marquardt (APSO_LM) to achieve faster convergence rate and to avoid local minima problem. These techniques are chosen since they provide faster training for solving pattern recognition problems using the numerical optimization technique.The performances of the proposed algorithm is evaluated using some bench mark of classification’s datasets. The results are compared with Artificial Bee Colony (ABC) Algorithm using Back Propagation Neural Network (BPNN) algorithm and other hybrid variants. Based on the experimental result, the proposed algorithms APSO_LM successfully demonstrated better performance as compared to other existing algorithms in terms of convergence speed and Mean Squared Error (MSE) by introducing the error and accuracy in network convergence

Thermoluminescence response of Ge-doped optical fiber dosimeters with different core sizes

Thermoluminescence (TL) properties of five different core diameter of 6 mol% germanium (Ge) doped optical fibers have been investigated for the purpose of TL dosimetry. The optical fiber dosimeter TL properties is compared with commercially available TLD-100 chips (LiF:Mg,Ti). Samples were irradiated using Cobalt-60 standard radiation source ranging from 1Gy to 10 Gy. These fibers show good linear dose response up to 10 Gy. Highest core diameter of Ge doped optical fiber (core 100 μm) provides the best response among all fibers. We observe the larger core fiber show better response than smaller core fiber. The relative sensitivity of 100 μm core optical fiber is 0.26 ± 0.04 with respect to TLD-100 chip. © 2013 IEEE

In-field measurement and sampling technologies for monitoring quality in the sugarcane industry: a review

Reliable in-field quality measurement and sampling techniques are needed in the sugarcane industry to accommodate spatial variability in crop quality during harvesting. Existing in-field monitoring systems only monitor the crop yield and do not have the ability to measure product quality. This is a serious limitation for the industry in dealing with a significant quality variation across a field. Conventional technologies for measuring sugarcane quality in a laboratory have severe limitations for field use because they require complex sample preparation procedures especially to have clarified juice samples for each measurement. This review focuses on the use of current and new emerging precision agricultural sensing technologies for measuring product quality and describes their potential application and limitation for field use in the sugarcane industry. Optical spectroscopy is among the most promising technologies for measuring sugarcane quality on a harvester. The key considerations for development of a measurement method and sampling mechanism in the field are also discussed