Uday Pratap Singh

FCPN Approach for Uncertain Nonlinear Dynamical System with Unknown Disturbance

In this work, we have used a fuzzy counter-propagation network (FCPN) model to control different discrete-time, uncertain nonlinear dynamic systems with unknown disturbances. Fuzzy competitive learning (FCL) is used to process the weight connection and make adjustments between the instar and the outstar of the network. FCL paradigm adopts the principle of learning, used for calculation of the Best Matched Node (BMN) in the instar–outstar network. FCL provides a control of discrete-time uncertain nonlinear dynamic systems having dead zone and backlash. The errors like mean absolute error (MAE), mean square error (MSE), and best fit rate, etc. of FCPN are compared with networks like dynamic network (DN) and back propagation network (BPN). The FCL foretells that the proposed FCPN method gives better results than DN and BPN. The success and enactments of the proposed FCPN are validated through simulations on different discrete-time uncertain nonlinear dynamic systems and Mackey–Glass univariate time series data with unknown disturbances over BPN and DN.

Modified Chaotic Bat Algorithm Based Counter Propagation Neural Network for Uncertain Nonlinear Discrete Time System

Weight and bias connection are important features of neural networks, which is still challenging for researchers. In this work, we focus on initial weights and bias connection of counter propagation network (CPN) using modified chaotic bat algorithm (MCBA) i.e., MCBA-CPN for uncertain nonlinear systems and compare it with CPN using chaotic bat algorithm (CBA) i.e., CBA-CPN. Chaotic function is used for pulse frequency of bats in MCBA. We have implemented CBA and MCBA, which are based on the consideration of the global solution in the sound intensity adjustment. MCBA-CPN is applied on different uncertain nonlinear systems and Mackey–Glass time series data to test the concert in terms of prediction accuracy. Proposed method is validated through statistical testing like chi-square and t-test demonstrate that the difference between target and output of proposed method are acceptable. Finally, MCBA-CPN is applied to a real world problem for prediction of milk production data.

Optimization of neural network for nonlinear discrete time system using modified quaternion firefly algorithm: case study of Indian currency exchange rate prediction

Success of neural networks depends on an important parameter, initialization of weights and bias connections. This paper proposes modified quaternion firefly algorithm (MQFA) for initial optimal weight and bias connection to neural networks. The proposed modified quaternion firefly method is based on updating population, moving fireflies and best solution in quaternion space. The combination of modified quaternion firefly and neural network is developed with the scope of creating an improved balance between premature convergence and stagnation. The performance of the proposed method is tested on two nonlinear discrete time systems, Box–Jenkins time series data and exchange rate prediction of Indian currency. Results of the MQFA with back-propagation neural network (MQFA-BPNN) compared with existing differential evolution-based neural network and opposite differential evolution-based neural network. Results obtain using MQFA-BPNN envisage that this method is effective and provides better identification accuracy. Computational complexity of MQFA-BPNN is deliberated, and validation of proposed method is tested by statistical methods.

Fuzzy Counter Propagation Network for Freehand Sketches-Based Image Retrieval

In this paper, we present Fuzzy Counter Propagation Network (FCPN) for Sketch-Based Image Retrieval (SBIR) with collection of freehand sketches; trademark and clip art, etc., using feature descriptors. FCPN is combination of Counter Propagation Network (CPN) and Fuzzy Learning (FL). We use features descriptor like Histogram of Gradient (HOG) for freehand sketches/images and these features are used to the training of FCPN. Flicker dataset containing 33 different shape categories, is used for training and testing. Different similarity measure functions are discussed and used similarity between query by nonexpert sketchers and database. We compare proposed FCPN method with other existing Feed-forward Networks (FFN) and Pattern Recognition Network (PRN). Experimental results show that FCPN methods outperform over networks.

An Efficient Contrast Enhancement Technique Based on Firefly Optimization

In the modern environment, digital image processing is a very vital area of research. It is a process in which an input image and output might be either any image or some characteristics. In image enhancement process, input image, therefore, results are better than given input image for any particular application or set of objectives. Traditional contrast enhancement technique results in lightning of image, so here Discrete Wavelet transform is applied on image and modify only Low–Low band. In this presented technique, for enhancement of given image having low contrast apply Brightness Preserving Dynamic Histogram Equalization (BPHDE), Discrete Wavelet Transform (DWT), Thresholding of sub-bands of DWT, Firefly Optimization and Singular Value Decomposition (SVD). DWT divides image into 4 bands of different frequency: High–high (HH), High–low (HL), Low–high (LH), and Low–low (LL). First apply a contrast enhancement technique named brightness preserving dynamic histogram equalization technique for enhancement of a given low-contrast image and boosts the illumination, then apply Firefly optimization on these 4 sub-bands and thresholding applied, this optimized LL band information and given input image’s LL band values are passed through SVD and new LL band obtained. Through inverse discrete wavelet transform of obtained new LL band and three given image’s HH, HL, and LH band obtained an image having high contrast. Quantitative metric and qualitative result of presented technique are evaluated and compared with other existing technique. A result reveals that presented technique is a more effective strategy for enhancement of image having low contrast. The technique presented by this study is simulated on Intel I3 64-bit processor using MATLAB R2013b.

Discovering Optimal Patterns for Forensic Pattern Warehouse

As the need of investigative information is increasing at an exponential rate, extraction of relevant patterns out of huge amount of forensic data becomes more complex. Forensic pattern mining is a technique that deals with mining of the forensic patterns from forensic pattern warehouse in support of forensic investigation and analysis of the causes of occurrence of an event. But, sometimes those patterns do not provide certain analytical results and also may contain some noisy information with them. An approach through which optimal patterns or reliable patterns are extracted from forensic pattern warehouse which strengthen the decisions-making process during investigations has been proposed in the paper.

Biogeography particle swarm optimization based counter propagation network for sketch based face recognition

In this paper, we present a Biogeography Particle Swarm Optimization (BPSO) based Counter Propagation Network (CPN) i.e. BPSO-CPN for Sketch Based Face Recognition (SBFR) system. A new criterion of selecting exemplar vector using biogeography learning based PSO is used for optimization of Mean Square Error (MSE) between feature vector of sketch and photo. In this work, we use Histogram of Gradient (HOG) feature vector for similarity measures between sketch and photo. Select a sketch as query image from database and using BPSO-CPN to retrieves similar photos from database. Proposed BPSO-CPN method is tested on CUHK and IIITD sketch dataset containing about 1000 sketches and photos. The experimental result envisage that, BPSO-CPN gives promising results and achieves high precision as comparison with other existing methods and neural networks. Motivation behind this research work is to find missing or wanted persons who involve in antinational activities and it help investigating agencies to narrow down the suspects quickly.

Soft computing approaches for image segmentation: a survey

Image segmentation is the method of partitioning an image into a group of pixels that are homogenous in some manner. The homogeneity dependents on some attributes like intensity, color etc. Segmentation being a pre-processing step in image processing have been used in the number of applications like identification of objects to medical images, satellite images and much more. The taxonomy of an image segmentation methods collectively can be divided among two categories Traditional methods and Soft Computing (SC) methods. Unlike Traditional methods, SC methods have the ability to simulate human thinking and are flexible to work with their ownership function, have been predominantly applied to the task of image segmentation. SC techniques are tolerant of partial truth, imprecision, uncertainty, and approximations. Soft Computing approaches also having advantages of providing cost-effective, high performance and steadfast solutions. In this survey paper, our emphasis is on core SC approaches like Fuzzy logic, Artificial Neural Network, and Genetic Algorithm used for image segmentation. The contribution lies in the fact to present this paper to the researchers that explore state-of-the-art elaboration of almost all dimensions associated with the image segmentation. The idea is to encapsulate various aspects like emerging topics, methods, evaluation parameters, the problem associated with different type of images, databases, segmentation applications, and other resources so that, it could be advantageous for researchers to make effort in developing new methods for segmentation. The paper accomplishes with findings and concluding remarks.

Kohonen neural network model reference for nonlinear discrete time systems

In this work, an adaptive neural network like Kohonen neural network (KNN) model reference is used for tracking control of nonlinear system. Proposed adaptive Kohonen neural network (ADKNN) are used to minimize the error between output and target signal for nonlinear discrete-time systems. The ADKNN is a feed-forward neural network help for approximation of the nonlinearities in the industrial plant and main characteristic of the system is taken into account is disturbances in the system. Tracking error by the adaptive ADKNN based approximation system is an important characteristic for the design and analysis. It is shown in results that the preference of the error system is decisive to the solution of tracking control. Difference between ADKNN output and reference signal can be made arbitrarily small in the close neighbourhood of zero. The viability of the ADKNN is verified via simulation example of nonlinear system.

ADAPTIVE NEURAL NETWORK FOR SKETCH BASED IMAGE RETRIEVAL

In this paper, we present neural network approach for Sketch Based Image Retrieval (SBIR) using Histogram of Gradient (HOG) feature descriptor. This paper emphasis on back propagation Feed-forward Network (FFN) and Pattern Recognition Network (PRN) used for sketch based retrieval. Neural network is a popular tool used for pattern recognition and approximation of unknown nonlinear functions. We use features descriptor like Histogram of Gradient (HOG) for free hand and human face sketches and these features are used to training of network. Experimental results and analysis are based on CHUK and Flicker dataset, used for training and testing. Different similarity measure functions are discussed and used similarity between query by non-expert sketchers and database.