Intelligent swarm algorithms for optimizing nonlinear sliding mode controller for robot manipulator

This work introduces an accurate and fast approach for optimizing the parameters of robot manipulator controller. The approach of sliding mode control (SMC) was proposed as it documented an effective tool for designing robust controllers for complex high-order linear and nonlinear dynamic systems operating under uncertain conditions. In this work Intelligent particle swarm optimization (PSO) and social spider optimization (SSO) were used for obtaining the best values for the parameters of sliding mode control (SMC) to achieve consistency, stability and robustness. Additional design of integral sliding mode control (ISMC) was implemented to the dynamic system to achieve the high control theory of sliding mode controller. For designing particle swarm optimizer (PSO) and social spider optimization (SSO) processes, mean square error performances index was considered. The effectiveness of the proposed system was tested with six degrees of freedom robot manipulator by using (PUMA) robot. The iteration of SSO and PSO algorithms with mean square error and objective function were obtained, with best fitness for (SSO) =4.4876 -6 and (PSO)=3.4948 -4

Training Artificial Neural Network Using Back-Propagation & Particle Swarm Optimization for Image Skin Diseases

This work is devoted to compression Image Skin Diseases by using Discrete Wavelet Transform (DWT) and training Feed-Forward Neural Networks (FFNN) by using Particle Swarm Optimization(PSO) and compares it with Back-Propagation (BP) neural networks in terms of convergence rate and accuracy of results .The comparison between the two techniques will be mentioned. A MATLAB 6.5 program is used in simulation. The structure Artificial Neural Network (ANN) of training image skin diseases is proposed as follows: 1- The proposed structure of NN that performs three compressions Images Skin training by BP algorithms with log sigmoid activation function, and three neurons in output layer. 2- The proposed structure of FFNN using PSO that performs three compressions Images Skin with hardlim activation function, and three neurons in output layer. The results obtained using PSO are compared to those obtained using BP. Learning iterations (602-4700 epoch), convergence time (1sec.- 100 sec.), number of initial weights (1set - 75set), number of derivatives (0 - 38 derivatives) and accuracy (60% - 100%) are used as performance measurements. The obtained Mean Square Error (MSE) is 7 10 - to check the performance of algorithms. The results of the proposed neural networks performed indicate that PSO can be a superior training algorithm for neural networks, which is consistent with other research in the area

Integration of Swarm Intelligence and Artificial Neural Network for Medical Image Recognition

Neural network technology plays an important role in the development of new medical diagnostic assistance or what is known as “computer aided” that based on image recognition.Thispaper study the method used integration of back propagation neural network and Particle Swarm Optimizing (PSO) in parts of recognition the XRay of lungs for two disease cases (cancer and TB) along with the normal case. The experiments show that the improvement of algorithms for recognition side has achieved a good result reached to 88.398% for input image size 1024 pixel and 500 population size. The efficiency and recognition testes for training method was performed and reported in this pape

Training Artificial Neural Networks by PSO to Perform Digital Circuits Using Xilinx FPGA

One of the major constraints on hardware implementations of Artificial Neural Networks (ANNs) is the amount of circuitry required to perform the multiplication process of each input by its corresponding weight and there subsequent addition. Field Programmable Gate Array (FPGA) is a suitable hardware IC for Neural Network (NN) implementation as it preserves the parallel architecture of the neurons in a layer and offers flexibility in reconfiguration and cost issues. In this paper the adaption of the ANN weights is proposed using Particle Swarm Optimization (PSO) as a mechanism to improve the performance of ANN and also for the reduction in the ANN hardware. For this purpose we modified the MATLAB PSO toolbox to be suitable for the taken application. In the proposed design training is done off chip then the fully trained design is download into the chip, in this way less circuitry is required. This paper executes four bit Arithmetic Logic Unit (ALU) implemented using Xilinx schematic design entry tools as an example for the implementation of digital circuits using ANN trained by PSO algorithm

Mirrorless all‐optical bistability in bacteriorhodopsin

We report direct observations of all‐optical mirrorless bistability associated with saturable absorption in three kinds of bacteriorhodopsin (BR) samples: wild‐type BR in water solution and dispersed in thin films of a polymer matrix as well as water solution of the genetically engineered mutant BRD96N. The experiments are carried out with picosecond pulses at 532 nm. The values measured for the saturation intensity are explained in terms of the relaxation of the excited M state population to the B state of the BR photocycle for the three kinds of samples studied

OPTOELECTRONIC IMPLEMENTATION OF ARTIFICIALNEURAL NETWORK: PERCEPTRON LEARNING RULE AND MCATEGORYCLASSIFIER

Single neuron perceptron is designed as a classifier of two different classes using the hardlimiter activation function (i.e. in the absence of light, and presence of light). An example is designed and tested so that the proposed circuit learned different categories and then used as a classifier for two different classes because of the use of single neuron. Additional electronic circuits were used for computation processes. The Computer simulation results indicate stable solution that compares with theoretical results. Single layer perceptron M-category classifier is designed as a classifier for more than two classes. An example is designed and tested for the verification. The example learns after (5) iterations. Computer simulation results indicate stable solution that compares favorably with theoretical results

Towards a uniform earthquake risk model for Europe

Seismic risk has been the focus of a number of European projects in recent years, but there has never been a concerted effort amongst the research community to produce a uniform European risk model. The H2020 SERA project has a work package that is dedicated to that objective, with the aim being to produce an exposure model, a set of fragility/vulnerability functions, and socio-economic indicators in order to assess probabilistic seismic risk at a European scale. The partners of the project are working together with the wider seismic risk community through web tools, questionnaires, workshops, and meetings. All of the products of the project will be openly shared with the community on both the OpenQuake platform of the Global Earthquake Model (GEM) and the web platform of the European Facilities for Earthquake Hazard and Risk (EFEHR)

The European Seismic Risk Model 2020 (ESRM20)

This study describes the development of the various components of the European Seismic Risk Model 2020 (ESRM2020) which will be able to generate, using open-source software developed by the GEM Foundation (the Open Quake-engine), a number of Europe-wide risk metrics including average annualised human and economic losses (AAL), probable maximum losses (PML), and risk maps showing the losses for specific return periods or scenario events. The latest developments towards pan-European exposure models for residential and non-residential buildings and fragility/vulnerability models for damage, economic loss and casualty assessment will be presented. For engineered buildings within the exposure model (reinforced concrete, steel), a simulated design is undertaken using the key aspects of seismic design codes in force across Europe over the past 100 years. The designed MDOF building is then transformed to a SDOF model and nonlinear dynamic analyses are run using a large number of ground motion records, after which cloud analysis is used to develop the fragility functions. For non-engineered buildings (unreinforced masonry, confined masonry, adobe), the SDOF models have been directly developed from simplified formulae, experimental tests and previous studies. Collaboration from local experts at various stages of the model development, initiated through workshops, is an important component of the model, as well as the extensive calibration and validation

Comparisons among the five ground-motion models developed using RESORCE for the prediction of response spectral accelerations due to earthquakes in Europe and the Middle East

This article presents comparisons among the five ground-motion models described in other articles within this special issue, in terms of data selection criteria, characteristics of the models and predicted peak ground and response spectral accelerations. Comparisons are also made with predictions from the Next Generation Attenuation (NGA) models to which the models presented here have similarities (e.g. a common master database has been used) but also differences (e.g. some models in this issue are nonparametric). As a result of the differing data selection criteria and derivation techniques the predicted median ground motions show considerable differences (up to a factor of two for certain scenarios), particularly for magnitudes and distances close to or beyond the range of the available observations. The predicted influence of style-of-faulting shows much variation among models whereas site amplification factors are more similar, with peak amplification at around 1s. These differences are greater than those among predictions from the NGA models. The models for aleatory variability (sigma), however, are similar and suggest that ground-motion variability from this region is slightly higher than that predicted by the NGA models, based primarily on data from California and Taiwan