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

Derivation of consistent hard rock (1000<Vs<3000 m/s) GMPEs from surface and down-hole recordings: Analysis of KiK-net data

A key component in seismic hazard assessment is the estimation of ground motion for hard rock sites, either for applications to installations built on this site category, or as an input motion for site response computation. Empirical ground motion prediction equations (GMPEs) are the traditional basis for estimating ground motion while VS30 is the basis to account for site conditions. As current GMPEs are poorly constrained for VS30 larger than 1000 m/s, the presently used approach for estimating hazard on hard rock sites consists of “host-to-target” adjustment techniques based on VS30 and κ0 values. The present study investigates alternative methods on the basis of a KiK-net dataset corresponding to stiff and rocky sites with 500 < VS30 < 1350 m/s. The existence of sensor pairs (one at the surface and one in depth) and the availability of P- and S-wave velocity profiles allow deriving two “virtual” datasets associated to outcropping hard rock sites with VS in the range [1000, 3000] m/s with two independent corrections: 1/down-hole recordings modified from within motion to outcropping motion with a depth correction factor, 2/surface recordings deconvolved from their specific site response derived through 1D simulation. GMPEs with simple functional forms are then developed, including a VS30 site term. They lead to consistent and robust hard-rock motion estimates, which prove to be significantly lower than host-to-target adjustment predictions. The difference can reach a factor up to 3–4 beyond 5 Hz for very hard-rock, but decreases for decreasing frequency until vanishing below 2 Hz

Earthquake source parameters and scaling relationships in Hungary (central Pannonian basin)

Abstract Fifty earthquakes that occurred in Hungary (central part of the Pannonian basin) with local magnitude ML ranging from 0.8 to 4.5 have been analyzed. The digital seismograms used in this study were recorded by six permanent broad-band stations and twenty short-period ones at hypocentral distances between 10 and 327 km. The displacement spectra for P- and SH-waves were analyzed according to Brune’s source model. Observed spectra were corrected for path-dependent attenuation effects using an independent regional estimate of the quality factor QS. To correct spectra for near-surface attenuation, the k parameterwas calculated, obtaining it fromwaveforms recorded at short epicentral distances. The values of the k parameter vary between 0.01 to 0.06 s with a mean of 0.03 s for P-waves and between 0.01 to 0.09 s with a mean of 0.04 s for SH-waves. After correction for attenuation effects, spectral parameters (corner frequency and low-frequency spectral level) were estimated by a grid search algorithm. The obtained seismic moments range from4.21×1011 to 3.41×1015 Nm (1.7≤Mw ≤4.3). The source radii are between 125 and 1343 m. Stress drop values vary between 0.14 and 32.4 bars with a logarithmic mean of 2.59 bars (1 bar = 105 Pa). From the results, a linear relationship between local andmomentmagnitudes has been established. The obtained scaling relations show slight evidence of self-similarity violation. However, due to the high scatter of our data, the existence of self-similarity cannot be excluded

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

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

Understanding single-station ground motion variability and uncertainty (sigma) – Lessons learnt from EUROSEISTEST

Accelerometric data from the well-studied valley EUROSEISTEST are used to investigate ground motion uncertainty and variability. We define a simple local ground motion prediction equation (GMPE) and investigate changes in standard deviation (σ) and its components, the between-event variability (τ) and within-event variability (φ). Improving seismological metadata significantly reduces τ (30-50%), which in turn reduces the total σ. Improving site information reduces the systematic site-to-site variability, φS2S (20-30%), in turn reducing φ, and ultimately, σ. Our values of standard deviations are lower than global values from literature, and closer to path-specific than site-specific values. However, our data have insufficient azimuthal coverage for single-path analysis. Certain stations have higher ground-motion variability, possibly due to topography, basin edge or downgoing wave effects. Sensitivity checks show that 3 recordings per event is a sufficient data selection criterion, however, one of the dataset’s advantages is the large number of recordings per station (9-90) that yields good site term estimates. We examine uncertainty components binning our data with magnitude from 0.01 to 2 s; at smaller magnitudes, τ decreases and φSS increases, possibly due to κ and source-site trade-offs Finally, we investigate the alternative approach of computing φSS using existing GMPEs instead of creating an ad hoc local GMPE. This is important where data are insufficient to create one, or when site-specific PSHA is performed. We show that global GMPEs may still capture φSS, provided that: 1. the magnitude scaling errors are accommodated by the event terms; 2. there are no distance scaling errors (use of a regionally applicable model). Site terms (φS2S) computed by different global GMPEs (using different site-proxies) vary significantly, especially for hard-rock sites. This indicates that GMPEs may be poorly constrained where they are sometimes most needed, i.e. for hard rock

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