Development of Artificial Intelligent Techniques for Manipulator Position Control

Inspired by works in soft computing this research applies the constituents of soft computing to act as the "brain" that controls the positioning process of a robot manipulator's tool. This work combines three methods in artificial intelligence: fuzzy rules, neural networks, and genetic algorithm to form the soft computing plant uniquely planned for a six degree-of-freedom serial manipulator. The forward kinematics of the manipulator is made as the feedforward control plant while the soft computing plant replaces the inverse kinematics in the feedback loop. Fine manipulator positioning is first achieved from the learning stage, and later execution through forward kinematics after the soft computing plant proposes inputs and the iterations. It is shown experimentally that the technique proposed is capable of producing results with very low errors. Experiment A for example resulted the position errors onpx: 0.004%;py: 0.006%; andpz: 0.002%

Real-valued feature selection for process approximation and prediction

The selection of features for classification, clustering and approximation is an important task in pattern recognition, data mining and soft computing. For real-valued features, this contribution shows how feature selection for a high number of features can be implemented using mutual in-formation. Especially, the common problem for mutual information computation of computing joint probabilities for many dimensions using only a few samples is treated by using the Rènyi mutual information of order two as computational base. For this, the Grassberger-Takens corre-lation integral is used which was developed for estimating probability densities in chaos theory. Additionally, an adaptive procedure for computing the hypercube size is introduced and for real world applications, the treatment of missing values is included. The computation procedure is accelerated by exploiting the ranking of the set of real feature values especially for the example of time series. As example, a small blackbox-glassbox example shows how the relevant features and their time lags are determined in the time series even if the input feature time series determine nonlinearly the output. A more realistic example from chemical industry shows that this enables a better ap-proximation of the input-output mapping than the best neural network approach developed for an international contest. By the computationally efficient implementation, mutual information becomes an attractive tool for feature selection even for a high number of real-valued features

Optimal policy design for the sugar tax

Healthy nutrition promotions and regulations have long been regarded as a tool for increasing social welfare. One of the avenues taken in the past decade is sugar consumption regulation by introducing a sugar tax. Such a tax increases the price of extensive sugar containment in products such as soft drinks. In this article we consider a typical problem of optimal regulatory policy design, where the task is to determine the sugar tax rate maximizing the social welfare. We model the problem as a sequential game represented by the three-level mathematical program. On the upper level, the government decides upon the tax rate. On the middle level, producers decide on the product pricing. On the lower level, consumers decide upon their preferences towards the products. While the general problem is computationally intractable, the problem with a few product types is polynomially solvable, even for an arbitrary number of heterogeneous consumers. This paper presents a simple, intuitive and easily implementable framework for computing optimal sugar tax in a market with a few products. This resembles the reality as the soft drinks, for instance, are typically categorized in either regular or no-sugar drinks, e.g. Coca-Cola and Coca-Cola Zero. We illustrate the algorithm using an example based on the real data and draw conclusions for a specific local market

Bacteria Foraging Algorithm for Metamaterial Design and Optimization

Soft computing techniques are emerging as highly efficient global optimization techniques in the field of electromagnetics. These techniques along with the EM software have proved their efficiency in antenna engineering, wireless communication, absorber design and a few in the field of metamaterial structural analysis. Bacteria foraging algorithm, although has been used recently in controls, is still new to the field of metamaterial science and technology. In this paper, bacteria foraging algorithm (BFA) is used for design optimization of a double ring circular split ring resonator. Equivalent circuit analysis is used the EM tool for analysis of the CSRR. The aim of bacteria foraging algorithm is the estimation of structural parameters of the CSRR at a desired frequency range. Further the developed algorithm is proved through extraction of parameters of the optimized metamaterial structure. A comparative study with other soft computing techniques w.r.t. accuracy and computational time is provided

ProteinParser:a Community Based Tool for the Generation of a Detailed Protein Consensus and FASTA Output

Comparison of bioinformatic data is a common application in the life sciences and beyond. In this communication, a novel Java based software tool, ProteinParser, is outlined. This soft- ware tool calculates a detailed consensus, or most common, amino acid at a given position in an aligned protein set, whilst also generating a full consensus protein FASTA output. A second application of this software tool, computing a consensus amino acid given a toler- ance threshold, is also demonstrated. The phytase and the common bacterial �-lactamase proteins are analysed as ‘proof of concept’ examples. Consensus proteins, as generated by ProteinParser, are regularly utilised in the selection of residues for protein stabilisation muta- genesis; however, this widely applicable software tool will find many alternative applications in areas such as protein homology modelling

A Soft Computing Framework for Brain Tumor Detection through MRI Images

Brain Tumor is one of the deadly diseases that has taken the lives of many people. A tumor can be benign or malignant. Benign tumors are curable if detected at the early stage. In today’s modern era of medical technology, MRI (Magnetic Resonance Imaging) has proved to be an efficient method of detecting the presence of brain tumor in the patient. Proper detection of brain tumor is necessary for further treatment of the patient which is possible through accurate segmentation of the brain. Brain segmentation plays a vital role in brain tumor detection. Over the years many researchers have proposed different methods for brain tumor detection but use of soft computing tool is much more preferred as far as human error is concerned. Here, a method of classification of images with and without tumor is dictated using Artificial Neural Network (ANN). The ANN has been configured to detect the presence of tumor by using various parameters of Gray-Level Co-occurrence Matrix (GLCM).Keywords:Brain tumor, MRI, pre-processing, soft computing, neural networ