Step size self-adaptation for SGD

<p>Convergence and generalization are two crucial aspects of performance in neural networks. When analyzed separately, these properties may lead to contradictory results. Optimizing a convergence rate yields fast training, but does not guarantee the best generalization error. To avoid the conflict, recent studies suggest adopting a moderately large step size for optimizers, but the added value on the performance remains unclear. We propose the LIGHT function with the four configurations which regulate explicitly an improvement in convergence and generalization on testing. This contribution allows to: 1) improve both convergence and generalization of neural networks with no need to guarantee their stability; 2) build more reliable and explainable network architectures with no need for overparameterization. We refer to it as step size self-adaptation.</p>

Step size self-adaptation for SGD

<p>Convergence and generalization are two crucial aspects of performance in neural networks. When analyzed separately, these properties may lead to contradictory results. Optimizing a convergence rate yields fast training, but does not guarantee the best generalization error. To avoid the conflict, recent studies suggest adopting a moderately large step size for optimizers, but the added value on the performance remains unclear. We propose the LIGHT function with the four configurations which regulate explicitly an improvement in convergence and generalization on testing. This contribution allows to: 1) improve both convergence and generalization of neural networks with no need to guarantee their stability; 2) build more reliable and explainable network architectures with no need for overparameterization. We refer to it as step size self-adaptation.</p>

Single Point Incremental Forming Springback Reduction Using Edge Stiffener

Single point incremental forming (SPIF) is a dieless forming process which uses local deformations to form complex geometries. This is achieved through the use of a typically hemispherical tipped forming tool. Several variations of SPIF have been developed to improve the performance of this process. This includes the use of a partial die which is placed on the back-side of the material. The forming tool is then able to press the material into this partial die. Another method is to utilize a clamping fixture with a periphery that closely matches that of the desired geometry. While both of these methods improve the performance of SPIF, they also require dedicated fixturing. While these modifications still present an advantage over traditional stamping, it is desirable to avoid the use of any geometry-specific equipment. Springback is a significant issue when performing traditional SPIF. Springback can occur in two different ways: local and global. Local springback results from the elastic deformations created outside the region located directly beneath the forming tool. This causes poor accuracy as a result. Compensation methods have been developed to overcome this type of springback but are faced with certain limitations. Global springback refers to the springback experienced once the material is removed from its clamping fixture. This springback is a result of all residual stresses produced during forming. This springback is much more difficult to reduce and often requires annealing the workpiece subsequent to forming. A toolpath approach is explored herein as a method to reduce springback without the use of geometry-specific equipment. The toolpath developed begins at the edge of the clamping fixture, regardless of the geometry shape, and forms the flashing material prior to the desired geometry. By starting the toolpath along the edge of the fixture, elastic deformations are minimized. Additionally, the work hardening produced during this forming acts as a stiffener for the desired geometry, which behaves as a frame which matches the periphery of the desired geometry. This method was experimentally tested for its accuracy improvements when forming a truncated pyramid from 5052 aluminum. The angle of this stiffener, the step size of the stiffener, and the size of the desired geometry were varied. The fixture dimensions were held constant. This method was found to reduce the overall springback of the part and increase the accuracy of the resulting geometry. Furthermore, it was found that a large step size can be used to form the stiffener section of the part. By using a large step size, the time it takes to form this sacrificial region is minimized.

A novel P&OT-Neville’s interpolation MPPT scheme for maximum PV system energy extraction

Photovoltaic (PV) system posses an optimal operating pointing, termed as Maximum Power Point (MPP). Using Maximum Power Point Tracking (MPPT) algorithm, MPP of PV system has to be tracked continuously in any climatic conditions. In general, traditional Perturb and Observe (P&OT) MPP tracker is widely used among existing controllers. But, P&OT fails to harvest maximum power from solar panel, in addition oscillations around MPP results in low efficiency of the PV system. The contradiction involved in the traditional controller can be addressed as P&OT operates with a fixed step size. Hence, with large step size MPP can be reached quickly but the magnitude of oscillations around MPP are high. Similarly, when P&OT operated with tiny step size magnitude of oscillations can be reduced at the same time PV system consumes much time to reach MPP. In order to eliminate the contradiction involved with traditional MPPT scheme and effectively optimize PV system energy, this paper put forwards a hybrid MPPT scheme based on P&OT and Neville interpolation. The proposed scheme is executed in two stages. In the first stage, P&OT is operated with a large step size till the voltage reaches near to maximum point. In the second stage, Neville interpolation is used to find the maximum power point. The performance of the proposed scheme is compared with Golden Section Search (GSS) and P&OT MPPT controllers. With the proposed scheme the convergence time required to reach MPP is improved greatly. Experimental prototype is designed and developed to verify the performance of the proposed scheme. Experimental and simulation results provide enough evidence to show superiority of the proposed scheme.Article History: Received December 15th 2017; Received in revised form July 16th 2018; Accepted September 12th 2018; Available onlineHow to Cite This Article: Bhukya, M. N. and Kota, V. R. (2018) A Novel PandOT-Neville’s Interpolation MPPT Scheme for Maximum PV system energy extraction. International Journal of Renewable Energy Development, 7(3), 251-260https://dx.doi.org/10.14710/ijred.7.3.251-260

A novel P&OT-Neville’s interpolation MPPT scheme for maximum PV system energy extraction

Photovoltaic (PV) system posses an optimal operating pointing, termed as Maximum Power Point (MPP). Using Maximum Power Point Tracking (MPPT) algorithm, MPP of PV system has to be tracked continuously in any climatic conditions. In general, traditional Perturb and Observe (P&OT) MPP tracker is widely used among existing controllers. But, P&OT fails to harvest maximum power from solar panel, in addition oscillations around MPP results in low efficiency of the PV system. The contradiction involved in the traditional controller can be addressed as P&OT operates with a fixed step size. Hence, with large step size MPP can be reached quickly but the magnitude of oscillations around MPP are high. Similarly, when P&OT operated with tiny step size magnitude of oscillations can be reduced at the same time PV system consumes much time to reach MPP. In order to eliminate the contradiction involved with traditional MPPT scheme and effectively optimize PV system energy, this paper put forwards a hybrid MPPT scheme based on P&OT and Neville interpolation. The proposed scheme is executed in two stages. In the first stage, P&OT is operated with a large step size till the voltage reaches near to maximum point. In the second stage, Neville interpolation is used to find the maximum power point. The performance of the proposed scheme is compared with Golden Section Search (GSS) and P&OT MPPT controllers. With the proposed scheme the convergence time required to reach MPP is improved greatly. Experimental prototype is designed and developed to verify the performance of the proposed scheme. Experimental and simulation results provide enough evidence to show superiority of the proposed scheme.Article History: Received December 15th 2017; Received in revised form July 16th 2018; Accepted September 12th 2018; Available onlineHow to Cite This Article: Bhukya, M. N. and Kota, V. R. (2018) A Novel PandOT-Neville’s Interpolation MPPT Scheme for Maximum PV system energy extraction. International Journal of Renewable Energy Development, 7(3), 251-262https://dx.doi.org/10.14710/ijred.7.3.251-26

Novel Adaptive Bacteria Foraging Algorithms for Global Optimization

This paper presents improved versions of bacterial foraging algorithm (BFA). The chemotaxis feature of bacteria through random motion is an effective strategy for exploring the optimum point in a search area. The selection of small step size value in the bacteria motion leads to high accuracy in the solution but it offers slow convergence. On the contrary, defining a large step size in the motion provides faster convergence but the bacteria will be unable to locate the optimum point hence reducing the fitness accuracy. In order to overcome such problems, novel linear and nonlinear mathematical relationships based on the index of iteration, index of bacteria, and fitness cost are adopted which can dynamically vary the step size of bacteria movement. The proposed algorithms are tested with several unimodal and multimodal benchmark functions in comparison with the original BFA. Moreover, the application of the proposed algorithms in modelling of a twin rotor system is presented. The results show that the proposed algorithms outperform the predecessor algorithm in all test functions and acquire better model for the twin rotor system.