Construction of fuzzy radial basis function neural network model for diagnosing prostate cancer

In this paper, we propose a construction of fuzzy radial basis function neural network model for diagnosing prostate cancer. A fuzzy radial basis function neural network (fuzzy RBFNN) is a hybrid model of logical fuzzy and neural network. The fuzzy membership function of the fuzzy RBFNN model input is developed using the triangle function. The fuzzy C-means method is applied to estimate the center and the width parameters of the radial basis function. The weight estimation is performed by various ways to gain the most accurate model. A singular value decomposition (SVD) is exploited to address this process. As a comparison, we perform other ways including back propagation and global ridge regression. The study also promotes image preprocessing using high frequency emphasis filter (HFEF) and histogram equalization (HE) to enhance the quality of the prostate radiograph. The features of the textural image are extracted using the gray level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM). The experiment results of fuzzy RBFNN are compared to those of RBFNN model. Generally, the performances of fuzzy RBFNN surpass the RBFNN in all accuracy calculation. In addition, the fuzzy RBFNN-SVD demonstrates the most accurate model for prostate cancer diagnosis

A performance evaluation of pruning effects on hybrid neural network

In this paper, we explore the pruning effects on a hybrid mode sequential learning algorithmnamely FuzzyARTMAP-prunable Radial Basis Function (FAM-PRBF) that utilizes FuzzyARTMAP to learn a training dataset and Radial Basis Function Network (RBFN) to performregression and classification. The pruning algorithm is used to optimize the hidden layer ofthe RBFN. The experimental results show that FAM-PRBF has successfully reduced thecomplexity and computation time of the neural network.Keywords: pruning; radial basis function network; fuzzy ARTMAP

Similarity networks for classification: a case study in the Horse Colic problem

This paper develops a two-layer neural network in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron transfer function is formed by composition of an adapted logistic function with the mean of the partial input-weight similarities. The resulting neuron model is capable of dealing directly with variables of potentially different nature (continuous, fuzzy, ordinal, categorical). There is also provision for missing values. The network is trained using a two-stage procedure very similar to that used to train a radial basis function (RBF) neural network. The network is compared to two types of RBF networks in a non-trivial dataset: the Horse Colic problem, taken as a case study and analyzed in detail.Postprint (published version

Multimodal decision-level fusion for person authentication

In this paper, the use of clustering algorithms for decision-level data fusion is proposed. Person authentication results coming from several modalities (e.g., still image, speech), are combined by using fuzzy k-means (FKM), fuzzy vector quantization (FVQ) algorithms, and median radial basis function (MRBF) network. The quality measure of the modalities data is used for fuzzification. Two modifications of the FKM and FVQ algorithms, based on a novel fuzzy vector distance definition, are proposed to handle the fuzzy data and utilize the quality measure. Simulations show that fuzzy clustering algorithms have better performance compared to the classical clustering algorithms and other known fusion algorithms. MRBF has better performance especially when two modalities are combined. Moreover, the use of the quality via the proposed modified algorithms increases the performance of the fusion system

Deteksi Dini Kanker Serviks dengan Model Fuzzy Radial Basis Fuction Neural NetWork (FRBFNN) DAN DETEKSI TEPI (EDGE DETECTION) METODE SOBEL

Fuzzy Radial Basis Function Neural Network (FRBFNN) merupakan gabungan model jaringan syaraf tiruan dan logika fuzzy yang dapat digunakan untuk deteksi dini kanker serviks. Tujuan skripsi ini adalah mendeskripsikan prosedur dan hasil pembentukan model Fuzzy Radial Basis Function Neural Network (FRBFNN) dengan preprocessing deteksi tepi (edge detection) metode Sobel untuk mendeteksi dini kanker serviks pada hasil citra Pap smear. Pada penelitian ini model yang digunakan untuk mendeteksi dini kanker serviks adalah model FRBFNN. Dalam proses penelitian menggunakan citra Pap smear serviks yang diperoleh dari Instalasi Patologi Anatomi RSUD. Prof. Dr. Margono Soekarjo Purwokerto. Data yang digunakan berjumlah 83 citra yang terdiri dari 14 citra Pap smear serviks normal dan 69 citra Pap smear serviks abnormal. Prosedur awal pembentukan model FRBFNN untuk mendeteksi dini kanker serviks adalah preprocessing citra dengan melakukan deteksi tepi (edge detection) metode Sobel. Selanjutnya menggunakan metode Gray Level Run Length Matrix (GLRLM), kemudian membagi data menjadi 2 bagian yaitu 75% data training dan 25% data testing. Variabel input yang digunakan adalah 7 fitur hasil ekstraksi metode GLRLM dan variabel output adalah diagnosa dari citra Pap smear. Pembelajaran FRBFNN terbagi menjadi 4 tahap, yaitu fuzzifikasi dari 7 fitur dengan menggunakan fungsi keanggotaan trapesium, menentukan nilai pusat dan jarak menggunakan Fuzzy C-Means (FCM) Clustering, menentukan jumlah neuron pada lapisan tersembunyi, dan menentukan bobot jaringan dengan menggunakan metode Global Ridge Regression. Hasil penelitian menunjukkan bahwa model FRBFNN terbaik yang diperoleh adalah 7 neuron pada lapisan input, 21 neuron pada lapisan input fuzzy, 7 neuron serta 1 neuron bias pada lapisan tersembunyi, dan 1 neuron pada lapisan output. Persentase akurasi, sensitivitas, dan spesitifitas model FRBFNN untuk mendeteksi dini kanker serviks secara berurutan adalah 82,54%, 100%, 8,33% untuk data training dan 90%, 100%, 0% untuk data testing. Kata kunci: Kanker Serviks, Fuzzy Radial Basis Function Neural Network (FRBFNN), Deteksi Tepi (Edge Detection), Metode Sobel

A Novel Fuzzy Clustering Algorithm for Radial Basis Function Neural Network

A Fuzzy Radial basis function neural network (FRBFNN) classifier is proposed in the framework of Radial basis function neural network (RBFNN). This classifier is constructed using class-specific fuzzy clustering to form the clusters which represent the neurons i.e. fuzzy set hyperspheres (FSHs) in the hidden layer of FRBFNN. The creation of these FSHs is based on the maximum spread from inter-class information and intra-class fuzzy membership mechanism. The proposed approach is fast, independent of parameters, and shows good data visualization. The Least mean square training between the hidden layer to output layer in RBFNN is avoided, thus reduces the time complexity. The FRBFNN is trained quickly due to the fast converge of input data to form the FHSs in the hidden layer. The output is determined by the union operation of the FHSs outputs which are connected to the class nodes in the output layer. The performance of the proposed FRBFNN is compared with the other RBFNNs using ten benchmark datasets. The empirical findings demonstrate that the proposed FRBFNN is highly efficient classifier for pattern recognition

MODEL FUZZY RADIAL BASIS FUNCTION NEURAL NETWORK (FRBFNN) UNTUK MERAMALKAN KEBUTUHAN LISTRIK DI PROVINSI DAERAH ISTIMEWA YOGYAKARTA

Fuzzy Radial Basis Function Neural Network (FRBFNN) adalah model penggabungan antara konsep logika fuzzy dengan Radial Basis Function Neural Network (RBFNN). Pada FRBFNN data input yang awalnya berupa nilai crisp diubah ke nilai fuzzy, dan data output yang berupa nilai fuzzy diubah ke nilai crisp. Tujuan dari penelitian ini adalah untuk menjelaskan prosedur peramalan kebutuhan listrik dan meramalkan kebutuhan listrik di Provinsi Daerah Istimewa Yogyakarta. Prosedur pembentukan model FRBFNN yaitu (1) menentukan input dengan melihat lag yang signifikan pada plot autokorelasi, (2) membagi data menjadi 2 yaitu data training dan data testing, (3) fuzzifikasi, (4) menentukan nilai pusat dan jarak menggunakan metode K-Means clustering, (5) membangun model FRBFNN dengan melihat nilai MAPE (Mean Absolute Percent Error) dan MSE (Mean Squares Error) terkecil (6) menguji model yang terbentuk dengan uji white noise dengan melihat plot ACF (Autocorrelation Function) dan PACF (Partial Autocorrelation Function) data residual. Pada penelitian ini, data yang digunakan adalah data time series dari kebutuhan listrik di Provinsi Daerah Istimewa Yogyakarta bulan Januari 2007 hingga Desember 2015. Fungsi keanggotaan yang digunakan adalah fungsi keanggotaan segitiga dengan 3 himpunan fuzzy. Arsitektur terbaik didapatkan 10 input dan 6 neuron tersembunyi dengan fungsi aktivasi Gaussian. Pembagian data training dan data testing hingga didapatkan model terbaik dengan kombinasi 75% dan 25%. Hasil MAPE data training dan data testing pada model terbaik dari penelitian ini adalah 7,9426% dan 9,7347%

Extending the functional equivalence of radial basis functionnetworks and fuzzy inference systems

We establish the functional equivalence of a generalized class of Gaussian radial basis function (RBFs) networks and the full Takagi-Sugeno model (1983) of fuzzy inference. This generalizes an existing result which applies to the standard Gaussian RBF network and a restricted form of the Takagi-Sugeno fuzzy system. The more general framework allows the removal of some of the restrictive conditions of the previous result