Smriti Srivastava

New fuzzy wavelet neural networks for system identification and control

By utilizing some of the important properties of wavelets like denoising, compression, multiresolution along with the concepts of fuzzy logic and neural network, new two fuzzy wavelet neural networks (FWNNs) are proposed for approximating any arbitrary non-linear function, hence identifying a non-linear system. The output of discrete wavelet transform (DWT) block, which receives the given inputs, is fuzzified in the proposed two methods: one using compression property and other using multiresolution property. We present a new type of fuzzy neuron model, each non-linear synapse of which is characterized by a set of fuzzy implication rules with singleton weights in their consequents. It is shown that noise and disturbance in the reference signal are reduced with wavelets and also the variation of somatic gain, the parameter that controls the slope of the activation function in the neural network, leads to more accurate output. Identification results are found to be accurate and speed of their convergence is fast. Next, we simulate a control system for maintaining the output at a desired level by using the identified models. Self-learning FNN controller has been designed in this simulation. Simulation results show that the controller is adaptive and robust.

Type-2 fuzzy wavelet networks (T2FWN) for system identification using fuzzy differential and Lyapunov stability algorithm

We propose a novel method for the identification of non-linear system by utilizing some of the important properties of wavelets like denoising, compression, multiresolution along with the concepts of fuzzy logic. Two new type-2 fuzzy wavelet networks (T2FWNs) are proposed here. These T2FWNs can handle rule uncertainties in a better way because of using the type-2 fuzzy sets in modeling and fuzzy differential (FD) and Lyapunov stability during learning. Lot of work has been done in the identification of non-linear system by using the models based on type-1 fuzzy logic system (FLS). But in practice they are unable to handle uncertainties in the rules. The robustness of the system is assured by Lyapunov stability (LS). Also we have explored the properties of wavelets and FLS to handle the uncertainties efficiently. As the stability of the model is highly dependent on the learning of the system we use Lyapunov stability in combination with fuzzy differential. FD gives the range of variation of parameters having lower and upper bound in which the system is stable. The performance of T2FWN is compared with type-1 FLS, FWN [D.W.C. Ho, P.-A. Zhang, J. Xu, Fuzzy wavelet networks for function learning, IEEE Trans. Fuzzy Syst. 9 (February (1)) 2000] and FWNN [S. Srivastava, M. Singh, M. Hanmandlu, A.N. Jha, New fuzzy wavelet neural networks for system identification and control, Intl. J. Appl. Soft Comput. 6 (November (I)) 2005, 1-17]. It is shown that noise and disturbance in the reference signal are reduced with wavelets. A comparison of three learning algorithms: (i) gradient descent (GD) (ii) a combination of Lyapunov stability and fuzzy differential (LSFD) and, (iii) a combination of (i) and (ii) is done.

Gait based authentication using gait information image features

The information set that widens the fuzzy set theory.A spatiotemporal statistical gait representation, which expresses the statistics of motion patterns.The superiority of our features demonstrated by testing them on changed co-variant conditions (i.e. clothing, carrying) and change in speed.The performance of the new features is quantified through measures like cumulative match characteristics (CMC). Human gait, a soft biometric helps to recognize people by the manner, they walk. This paper presents gait image features based on the information set theory, henceforth these are called gait information image features. The information set stems from a fuzzy set with a view to represent the uncertainty in the information source values using the entropy function. The proposed gait information image (GII) is derived by applying the concept of information set on the frames in one gait cycle and two features named gait information image with energy feature (GII-EF) and gait information image with sigmoid feature (GII-SF) are extracted. Nearest neighbor (NN) classifier is applied to identify the gait. The proposed features are tested on Casia-B dataset, SOTON small database with variations in clothing and carrying conditions and on OU-ISIR Treadmill B database with large variation in clothing conditions. Moreover, experiments are carried out on OU-ISIR Treadmill A database with slight variation in the walking speeds to demonstrate the robustness of the proposed features.

A new Kernelized hybrid c-mean clustering model with optimized parameters

A possibilistic approach was initially proposed for c-means clustering. Although the possibilistic approach is sound, this algorithm tends to find identical clusters. To overcome this shortcoming, a possibilistic Fuzzy c-means algorithm (PFCM) was proposed which produced memberships and possibilities simultaneously, along with the cluster centers. PFCM addresses the noise sensitivity defect of Fuzzy c-means (FCM) and overcomes the coincident cluster problem of possibilistic c-means (PCM). Here we propose a new model called Kernel-based hybrid c-means clustering (KPFCM) where PFCM is extended by adopting a Kernel induced metric in the data space to replace the original Euclidean norm metric. Use of Kernel function makes it possible to cluster data that is linearly non-separable in the original space into homogeneous groups in the transformed high dimensional space. From our experiments, we found that different Kernels with different Kernel widths lead to different clustering results. Thus a key point is to choose an appropriate Kernel width. We have also proposed a simple approach to determine the appropriate values for the Kernel width. The performance of the proposed method has been extensively compared with a few state of the art clustering techniques over a test suit of several artificial and real life data sets. Based on computer simulations, we have shown that our model gives better results than the previous models.

Identification and Control of a Nonlinear System using Neural Networks by Extracting the System Dynamics

The present paper discusses an important issue of identification and control of a nonlinear dynamical system using neural network. A novel method based on neural network model has been developed here which makes the task of calculating the system parameters simple in comparison to the methods reported so far for the extraction of system dynamics of a nonlinear system. The updated parameters of the system are obtained using a learning algorithm and the controller is tuned accordingly. Further the system stability is compared with that obtained by existing networks and the simulated results show that our method is superior.

Choquet fuzzy integral based modeling of nonlinear system

For dealing with the adjacent input fuzzy sets having overlapping information, non-additive fuzzy rules are formulated by defining their consequent as the product of weighted input and a fuzzy measure. With the weighted input, need arises for the corresponding fuzzy measure. This is a new concept that facilitates the evolution of new fuzzy modeling. The fuzzy measures aggregate the information from the weighted inputs using the λ-measure. The output of these rules is in the form of the Choquet fuzzy integral. The underlying non-additive fuzzy model is investigated for identification of non-linear systems. The weighted input which is the additive S-norm of the inputs and their membership functions provides the strength of the rules and fuzzy densities required to compute fuzzy measures subject to q-measure are the unknown functions to be estimated. The use of q-measure is a powerful way of simplifying the computation of @l-measure that takes account of the interaction between the weighted inputs. Two applications; one real life application on signature verification and forgery detection, and another benchmark problem of a chemical plant illustrate the utility of the proposed approach. The results are compared with those existing in the literature.

Gait recognition using gait Gaussian image

In this paper, we proposed a new spatio-temporal based method for human gait recognition, named as Gait Gaussian Image (GGI). Gait Gaussian image is a period based gait technique, which is used for feature extraction of gait image over a gait cycle. The features derived from GGI are classified through Nearest neighbor method. Simulations and results are calculated on two benchmark datasets i.e. CASIA-B and Soton. Experimental results show the efficiency and effectiveness of the proposed method.

GFM-Based Methods for Speaker Identification

This paper presents three novel methods for speaker identification of which two methods utilize both the continuous density hidden Markov model (HMM) and the generalized fuzzy model (GFM), which has the advantages of both Mamdani and Takagi-Sugeno models. In the first method, the HMM is utilized for the extraction of shape-based batch feature vector that is fitted with the GFM to identify the speaker. On the other hand, the second method makes use of the Gaussian mixture model (GMM) and the GFM for the identification of speakers. Finally, the third method has been inspired by the way humans cash in on the mutual acquaintances while identifying a speaker. To see the validity of the proposed models [HMM-GFM, GMM-GFM, and HMM-GFM (fusion)] in a real-life scenario, they are tested on VoxForge speech corpus and on the subset of the 2003 National Institute of Standards and Technology evaluation data set. These models are also evaluated on the corrupted VoxForge speech corpus by mixing with different types of noisy signals at different values of signal-to-noise ratios, and their performance is found superior to that of the well-known models.

Face Detection Using Fuzzy Logic and Skin Color Segmentation in Images

Face detection is one of the challenging problems in image processing. A novel face detection system is presented in this paper and we propose a new approach using Takagi-Sugeno (T-S) fuzzy model and Hue Saturation and Value (HSV) color model. The algorithm uses fuzzy classifier in conjunction with HSV color model to quickly locate faces in the image. The fuzzy classifier basically examines small windows of an image to detect presence of faces in that window and the HSV color model detects the skin in the image. The algorithm is characterized by its simplicity and inexpensive computational requirements. The method shows that our system has comparable performance in terms of detection rates and false positive rates.

Fractional-Order PID Controller Design for Speed Control of DC Motor

— This paper deals speed control of a DC motor using fractional-order control. Fractional calculus provides novel and higher performance extensions for fractional order proportional integral and derivative (FOPID) controller. In this paper, the parameters of the FOPID controller are optimally learned by using Genetic Algorithm (GA), and the optimization performance target is chosen as the integral of the absolute error (IAE). Simulation results show that the FOPID controller performs better than the integer order PID controller.