Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

Outliers Resistant Learning Algorithm for Radial-basis-fuzzy-wavelet-neural Network in Stomach Acute Injury Diagnosis Tasks

In this paper an outliers resistant learning algorithm for the radial-basis-fuzzy-wavelet-neural network based on R. Welsh criterion is proposed. Suggested learning algorithm under consideration allows the signals processing in presence of significant noise level and outliers. The robust learning algorithm efficiency is investigated and confirmed by the number of experiments including medical applications

Fuzzy Logic

Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems

Application of Fractal and Wavelets in Microcalcification Detection

Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection

Составной адаптивный вэйвлон и алгоритм его обучения

Рассмотрена структура составного адаптивного вэйвлона и его алгоритм обучения. Предложен алгоритм, обладающий повышенной скоростью сходимости и обеспечивающий улучшенные аппроксимирующие свойства благодаря настройке всех параметров вэйвлет-фунций. Структура адаптивного вэйвлона может быть использована как строительный блок более сложных вычислительных конструкций.Розглянуто структуру складеного адаптивного вейвлона та його алгоритм навчання. Запропонований алгоритм має підвищену швидкість збіжності та забезпечує покращені апроксимуючі властивості завдяки настроюванню усіх параметрів вейвлет-функцій. Структура адаптивного вейвлона може бути використана як будівельний блок більш складних обчислювальних конструкцій.A compartmental adaptive wavelon and its learning algorithm are considered. A learning algorithm is suggested which has an increased convergence rate and provides the improved approximating properties because of the all wavelet parameters tuning. The suggested adaptive wavelon structure can be used as the block of more complex computational architecture

Adaptive self-recurrent wavelet neural network and sliding mode controller/observer for a slider crank mechanism

In this paper, a novel control strategy based on an adaptive Self-Recurrent Wavelet Neural Network (SRWNN) and a sliding mode controller/observer for a slider crank mechanism is proposed. The aim is to reduce the tracking error of the linear displacement of this mechanism while following a specified trajectory. The controller design consists of two parts. The first one is a sliding mode control strategy and the second part is an SRWNN controller. This controller is trained offline first, and then the SRWNN weights are updated online by the adaptive control law. Apart from the hybrid control strategy proposed in this paper, a velocity observer is implemented to replace the use of velocity sensors. The outcomes obtained in the numerical experiment section prove that the smallest tracking error is obtained for the linear and angular displacements in comparison with other strategies found in literature due to the uncertainty and disturbance rejection properties of the sliding mode and the self-recurrent wavelet neural network controllers.Peer ReviewedPostprint (author's final draft

Function Approximation Using Wavelet And Radial Basis Function Networks

Rangkaian Wavelet telah diperkenalkan sebagai proses suap depan bagi rangkaian neural yang disokong oleh teori wavelet. Rangkaian neural ini dapat digunakan secara langsung dalam penghampiran fungsi. Dalam disertasi ini, Rangkaian Wavelet dibuktikan sebagai salah satu sub-bahagian dalam kumpulan keturunan di mana rangkaian neural ini mempunyai sifat yang sama dengan kumpulan yang di namakan Fungsi Asas Radial Berpemberat. Hal ini juga berlaku bagi rangkaian neural yang mempunyai paradigma yang berlainan. Disertasi ini juga merangkumi pengkajian dalam Fungsi Asas Radial berperingkat 2.Fungsi ini juga dikenali sebagai Fungsi Asas Radial Piawai kerana mempunyai persamaan dimana fungsi ini akan bertindak sebagai Fungsi Asas Radial Piawai apabila fungsi exponent mempunyai sifat yang sarna dengan fungsi pengaktifan Gaussian apabila peringkat bagi eksponen n =2. The Wavelet Neural Network has been introduced as a special feedforward neural network supported by the wavelet theory. Such network can be directly used in function approximation problems. In this dissertation, wavelet networks are proven to be as well as many other neural paradigms, a specific case of generic paradigm named Weighted Radial Basis Functions Network. In this dissertation we will also investigate the WRBF- 2. WRBF-2 is standard RBF since the exponential function behaves as a Gaussian, due to the exponent n = 2

A Fuzzy Approach to the Synthesis of Cognitive Maps for Modeling Decision Making in Complex Systems

The object of this study is fuzzy cognitive modeling as a means of studying semistructured socio-economic systems. The features of constructing cognitive maps, providing the ability to choose management decisions in complex semistructured socio-economic systems, are described. It is shown that further improvement of technologies necessary for developing decision support systems and their practical use is still relevant. This work aimed to improve the accuracy of cognitive modeling of semistructured systems based on a fuzzy cognitive map of structuring nonformalized situations (MSNS) with the evaluation of root-mean-square error (RMSE) and mean average squared error (MASE) coefficients. In order to achieve the goal, the following main methods were used: systems analysis methods, fuzzy logic and fuzzy sets theory postulates, theory of integral wavelet transform, correlation and autocorrelation analyses. As a result, a new methodology for constructing MSNS was proposed—a map of structuring nonformalized situations that combines the positive properties of previous fuzzy cognitive maps. The solution of modeling problems based on this methodology should increase the reliability and quality of analysis and modeling of semistructured systems and processes under uncertainty. The analysis using open datasets proved that compared to the classical ARIMA, SVR, MLP, and Fuzzy time series models, our proposed model provides better performance in terms of MASE and RMSE metrics, which confirms its advantage. Thus, it is advisable to use our proposed algorithm in the future as a mathematical basis for developing software tools for the analysis and modeling of problems in semistructured systems and processes. Doi: 10.28991/ESJ-2022-06-02-012 Full Text: PD