Fuzzy Radial Basis Function Network Research Articles

Real-time estimation of hospital discharge using fuzzy radial basis function network and electronic health record data

Real-time estimation of hospital discharge using fuzzy radial basis function network and electronic health record data

Real-time estimation of hospital discharge using fuzzy radial basis function network and electronic health record data

Hospital resources are scarce and should be properly distributed and justified. Information about how long patients stays in critical intensive care units can provide significant benefits to hospital management resources and optimal admission planning. In this paper, we propose an approach for estimating intensive care unit length of stay using fuzzy radial basis function neural network model. The predictive performance of the model is compared to others using data collected over 13,587 admissions and 54 predictive factors from five critical units with discharges between 2001 and 2012. The proposed model compared to others demonstrated higher accuracy and better estimations. The three most influential factors in predicting length of stay at the early stage of pre-admission were demographic characteristics, admission type, and the first location within the hospital prior to critical unit admission. We have found about 63% of patients with multiple chronic conditions, stayed significantly longer in hospital. Enabling the proposed prediction model in clinical decision support system may serve as reference tools for communicating with patients and hospital managers.

Nonlinear system identification based on a self-organizing type-2 fuzzy RBFN

This paper presents a new self-evolving recurrent Type-2 Fuzzy Radial Basis Function Network (T2FRBFN) in which the weights are considered Gaussian type-2 fuzzy sets and uncertain mean in each RBF neuron. The capability of the proposed T2FRBFN for function approximation and dynamical system identification perform better than the conventional RBFN. A novel type-2 fuzzy clustering is presented to add or remove the hidden RBF neurons. For parameter learning, back-propagation with adaptive learning rate is used. Finally the proposed T2FRBFN is applied to identification of three nonlinear systems as case studies. A comparison between T2FRBFN and the conventional RBFN as well as the method of Rubio-Solis and Panoutsos (2015) is presented. Simulation results and their statistical description show that the proposed T2FRBFN perform better than the conventional RBFN.

Fuzzy radial basis function network for fuzzy regression with fuzzy input and fuzzy output

In this study, fuzzy regression (FR) models with fuzzy inputs and outputs are discussed. Some of the FR methods based on linear programming and fuzzy least squares in the literature are explained. Within this study, we propose a Fuzzy Radial Basis Function (FRBF) Network to obtain the estimations for FR model in the case that inputs and outputs are symmetric/nonsymmetric triangular fuzzy numbers. Proposed FRBF Network approach is a fuzzification of the inputs, outputs and weights of traditional RBF Network and it can be used as an alternative to FR methods. The FRBF Network approach is constructed on the basis of minimizing the square of the total difference between observed and estimated outputs. A simple training algorithm from the cost function of the FRBF Network through Backpropagation algorithm is developed in this study. The advantage of our proposed approach is its simplicity and easy computation as well as its performance. To compare the performance of the proposed method with those given in the literature, three numerical examples are presented.

Robust fuzzy RBF network based image segmentation and intelligent decision making system for carotid artery ultrasound images

In this paper, a robust image segmentation and intelligent decision making system for carotid artery ultrasound images is proposed. Medical images may have various types of inherent degradations due to imaging equipments, operating environment, etc. For instance, carotid artery ultrasound images are affected by low resolution, speckle noise, and wave interferences. Hence, robust medical image clustering technique is inevitable for obtaining accurate results in the subsequent stages. In this context, a robust fuzzy radial basis function network (RFRBFN) technique is proposed. The proposed technique modified fuzzy RBF algorithm by incorporating spatial information and a smoothing parameter into its objective function, consequently, the proposed technique is able to cope with noise related variations. To assess the effectiveness, the RFRBFN technique is applied to segment carotid artery ultrasound images and its performance has been evaluated against impulse, and Gaussian noises of various intensities. Performance comparison with existing methods shows that the proposed RFRBFN outperforms the existing fuzzy based c-means and RBF techniques in case of both noisy and noise-free images. Experiments on 200 real carotid artery ultrasound images reveal the proposed technique offers effective segmentation results. Finally, intima-media thickness is measured from the obtained segmented images and multi-layer backpropagation neural network is employed to classify the segmented images into normal or diseased subjects. The proposed intelligent decision making system can thus be used as a secondary observer for identification of plaque in the carotid artery.

Efficient environmental noise reduction system for speech enhancement via fuzzy-based extreme learning machine and fuzzy adaptive Wiener filter

This paper proposes the application of fuzzy-based extreme learning machine (FELM) and fuzzy-based adaptive Wiener filter for background noise reduction system to improve the signal to noise ratio (SNR) and to reduce the minimum mean square error (MMSE). The Wiener filter minimizes the mean square error and it provides better performance than the conventional filters. Though the background noise is uncertain, fuzzy inference systems (FIS) are proposed in ELM to classify the background noises and Wiener filters are proposed to update Wiener filter coefficients that will increase the SNR of the filtered speech signal. Fuzzy adaptive Wiener filter depends on the adaptation of the filter transfer function from sample to sample based on the signal statistics and it is implemented in time domain rather than in frequency domain to accommodate for the varying nature of the speech signal. The proposed FELM is compared with ELM and fuzzy radial basis function network (FRBFN) for noise classification and fuzzy adaptive Wiener filter is compared with Wiener and adaptive Wiener filters for noise cancellation. Simulation results show that FELM improves the percentage of classification by 9% than FRBFN, 4% than ELM and fuzzy adaptive Wiener filter improves the SNR by 8 dB than the conventional Wiener filter. The real-time implementation of the system is done using TMS320C6713 DSK starter kit. The real-time practical setup using DSK shows an improved SNR of 5 dB.

Strategies for intelligent drill wear prediction using multiple sensor signals

In this work, strategies for artificial intelligence (AI)-based drill flank wear prediction systems have been presented. Four AI-based models, namely, back propagation neural network (BPNN), radial basis function network (RBF), normalised radial basis function network (NRBF) and fuzzy radial basis function network (FRBF) have been used. Signals have been acquired from dynamometer, current sensor and accelerometer during drilling under different cutting conditions. Wear sensitive features in time-domain and frequency domain have been extracted. Different strategies, i.e., various combinations of the selected features and process parameters used as inputs to the AI models, are formulated. For most of the strategies, BPNN model gives better wear prediction results followed by NRBF model. But for noisy features such as features from vibration signal can be better related to drill wear using FRBF model.

An Intelligent Longitudinal Controller for Application in Semiautonomous Vehicles

We present a neuro-fuzzy controller for intelligent cruise control of semiautonomous vehicles. This paper addresses the problem of longitudinal control that aims at regulating the speed of the controlled vehicle in order to maintain constant time headway with respect to the vehicle in front. A fuzzy radial basis function network (FRBFN) longitudinal controller is designed to incorporate the merits of fuzzy logics as well as neural networks. The FRBFN is prestructured, and its parameters are configured such that they are associated with their physical meaning. The parameters of the output layer are learned online via gradient algorithm. An attractive feature of the proposed method is that it does not require the training data and the vehicle longitudinal dynamic model. Simulation results on a vehicle theoretical model are provided to demonstrate the effectiveness of this controller. In order to investigate the proposed control algorithms in real-life situations, a small-scaled vehicle with computer and sensors onboard is developed. Experimental results of a conventional PID controller and the FRBFN controller are provided for comparison.

A fuzzy Actor–Critic reinforcement learning network

One of the difficulties encountered in the application of reinforcement learning methods to real-world problems is their limited ability to cope with large-scale or continuous spaces. In order to solve the curse of the dimensionality problem, resulting from making continuous state or action spaces discrete, a new fuzzy Actor–Critic reinforcement learning network (FACRLN) based on a fuzzy radial basis function (FRBF) neural network is proposed. The architecture of FACRLN is realized by a four-layer FRBF neural network that is used to approximate both the action value function of the Actor and the state value function of the Critic simultaneously. The Actor and the Critic networks share the input, rule and normalized layers of the FRBF network, which can reduce the demands for storage space from the learning system and avoid repeated computations for the outputs of the rule units. Moreover, the FRBF network is able to adjust its structure and parameters in an adaptive way with a novel self-organizing approach according to the complexity of the task and the progress in learning, which ensures an economic size of the network. Experimental studies concerning a cart–pole balancing control illustrate the performance and applicability of the proposed FACRLN.

A soft relevance framework in content-based image retrieval systems

This paper presents a novel framework called fuzzy relevance feedback in interactive content-based image retrieval systems. Conventional binary labeling in relevance feedback requires crisp decisions to be made on the relevance of the retrieved images. This is restrictive as user interpretation of image similarity is imprecise and nonstationary in nature and may vary with respect to different information needs and perceptual subjectivity. It is, therefore, inadequate to model the user perception of image similarity with crisp binary logic. In view of this, we propose a soft relevance notion to integrate the users' fuzzy perception of visual contents into the framework of relevance feedback. A progressive fuzzy radial basis function network is proposed to learn the user information need by optimizing a cost function. An efficient gradient descent-based learning strategy is then employed to estimate the underlying network parameters. Experimental results based on a database of 10 000 images demonstrate the effectiveness of the proposed method.

Symbolic classification, clustering and fuzzy radial basis function network

Symbolic fuzzy classification is proposed using fuzzy radial basis function network, with fuzzy c-medoids clustering at the hidden layer. Symbolic objects include linguistic, nominal, boolean and interval-type of features, along with quantitative attributes. Classification and clustering in this domain involve the use of symbolic dissimilarity between the objects. Fuzzy memberships are used for appropriately handling uncertainty inherent in real-life decisions. The fuzzy radial basis function (FRBF) network here comprises an integration of the principles of radial basis function (RBF) network and fuzzy c-medoids clustering, for handling non-numeric data. The optimal number of hidden nodes is determined by using clustering validity indices, like normalized modified Hubert's statistic and Davies–Bouldin index, in the symbolic framework. The effectiveness of the symbolic fuzzy classification is demonstrated on real-life benchmark data sets. Comparison is provided with the performance of a decision tree.

Fuzzy radial basis function network: a parallel design

The fuzzy radial basis function (FRBF) network comprises an integration of the principles of a radial basis function (RBF) network and the fuzzy c-means (FCM) algorithm. A programmable paral...