PEMBELAJARAN JARINGAN SYARAF TIRUAN PROBABILISTIK BASIS RADIAL DENGAN MENGGUNAKAN ANALISIS SENSITIVITAS

Abstrak Makalah ini menyajikan algoritma pembelajaran bagi Radial Basis Probabilistic Neural Network berdasarkan keturunan gradien fungsi kesalahan. RBPNN terintegrasi dari jaringan saraf radial fungsi dasar (RBFNN) dan jaringan syaraf probabilistik (PNN). Langkah Kepekaan terhadap masukan atas dataset pelatihan berdasarkan turunan parsial dari model RBPNN dan aturan untuk memilih fitur berlebihan juga hadir. Analisis sensitivitas metode yang dapat meningkatkan efisiensi dan efektivitas jaringan saraf. Akhirnya, untuk mengevaluasi kinerja, pendekatan yang diusulkan kami menunjukkan melalui memberikan dua contoh kehidupan nyata kumpulan data. Kata Kunci: RBPNN, keturunan gradien, analisis sensitivitas, seleksi fitur, klasifikasi. Abstract This paper presents a learning algorithm for Radial Basis Probabilistic Neural Network based on gradient descent of error functions. RBPNN integrates of radial basis function neural networks (RBFNN) and probabilistic neural networks (PNN). Sensitivity measures to input over training dataset based on partial derivative of the RBPNN model and rule for selecting redundant feature is also present. Sensitivity analysis of that method can improved efficiency and effectiveness of the neural networks. Finally, to evaluate the performance, our proposed approaches are demonstrated trough giving two examples of real life data set. Keyword: RBPNN, gradient descent, sensitivity analysis, feature selection, classification

Real-Time Progressive Learning: Mutually Reinforcing Learning and Control with Neural-Network-Based Selective Memory

Memory, as the basis of learning, determines the storage, update and forgetting of the knowledge and further determines the efficiency of learning. Featured with a mechanism of memory, a radial basis function neural network (RBFNN) based learning control scheme named real-time progressive learning (RTPL) is proposed to learn the unknown dynamics of the system with guaranteed stability and closed-loop performance. Instead of the stochastic gradient descent (SGD) update law in adaptive neural control (ANC), RTPL adopts the selective memory recursive least squares (SMRLS) algorithm to update the weights of the RBFNN. Through SMRLS, the approximation capabilities of the RBFNN are uniformly distributed over the feature space and thus the passive knowledge forgetting phenomenon of SGD method is suppressed. Subsequently, RTPL achieves the following merits over the classical ANC: 1) guaranteed learning capability under low-level persistent excitation (PE), 2) improved learning performance (learning speed, accuracy and generalization capability), and 3) low gain requirement ensuring robustness of RTPL in practical applications. Moreover, the RTPL based learning and control will gradually reinforce each other during the task execution, making it appropriate for long-term learning control tasks. As an example, RTPL is used to address the tracking control problem of a class of nonlinear systems with RBFNN being an adaptive feedforward controller. Corresponding theoretical analysis and simulation studies demonstrate the effectiveness of RTPL.Comment: 16 pages, 15 figure

Data driven modeling using reinforcement learning in autonomous agents

Thesis (Master)--Izmir Institute of Technology, Mechanical Engineering, Izmir, 2003Includes bibliographical references (leaves: 61-66)Text in English; Abstract: Turkish and Englishvi, 75 leavesThis research has aspired to build a system which is capable of solving problems by means of its past experience, especially an autonomous agent that can learn from trial and error sequences. To achieve this, connectionist neural network architectures are combined with the reinforcement learning methods. And the credit assignment problem in multi layer perceptron (MLP) architectures is altered. In classical credit assignment problems, actual output of the system and the previously known data in which the system tries to approximate are compared and the discrepancy between them is attempted to be minimized. However, temporal difference credit assignment depends on the temporary successive outputs. By this new method, it is more feasible to find the relation between each event rather than their consequences.Also in this thesis k-means algorithm is modified. Moreover MLP architectures is written in C++ environment, like Backpropagation, Radial Basis Function Networks, Radial Basis Function Link Net, Self-organized neural network, k-means algorithm.And with their combination for the Reinforcement learning, temporal difference learning, and Q-learning architectures were realized, all these algorithms are simulated, and these simulations are created in C++ environment.As a result, reinforcement learning methods used have two main disadvantages during the process of creating autonomous agent. Firstly its training time is too long, and too many input parameters are needed to train the system. Hence it is seen that hardware implementation is not feasible yet. Further research is considered necessary

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Data generation and model usage for machine learning-based dynamic security assessment and control

The global effort to decarbonise, decentralise and digitise electricity grids in response to climate change and evolving electricity markets with active consumers (prosumers) is gaining traction in countries around the world. This effort introduces new challenges to electricity grid operation. For instance, the introduction of variable renewable energy generation like wind and solar energy to replace conventional power generation like oil, gas, and coal increases the uncertainty in power systems operation. Additionally, the dynamics introduced by these renewable energy sources that are interfaced through converters are much faster than those in conventional system with thermal power plants. This thesis investigates new operating tools for the system operator that are data-driven to help manage the increased operational uncertainty in this transition. The presented work aims to an- swer some open questions regarding the implementation of these machine learning approaches in real-time operation, primarily related to the quality of training data to train accurate machine- learned models for predicting dynamic behaviour, and the use of these machine-learned models in the control room for real-time operation. To answer the first question, this thesis presents a novel sampling approach for generating ’rare’ operating conditions that are physically feasible but have not been experienced by power systems before. In so doing, the aim is to move away from historical observations that are often limited in describing the full range of operating conditions. Then, the thesis presents a novel approach based on Wasserstein distance and entropy to efficiently combine both historical and ’rare’ operating conditions to create an enriched database capable of training a high- performance classifier. To answer the second question, this thesis presents a scalable and rigorous workflow to trade-off multiple objective criteria when choosing decision tree models for real-time operation by system operators. Then, showcases a practical implementation for using a machine-learned model to optimise power system operation cost using topological control actions. Future research directions are underscored by the crucial role of machine learning in securing low inertia systems, and this thesis identifies research gaps covering physics-informed learning, machine learning-based network planning for secure operation, and robust training datasets are outlined.Open Acces

Design , Development and Performance Evaluation of Intelligence Sensors

Many electronic devices, instruments and sensors exhibit inherent nonlinear input-output characteristics. Nonlinearity also creeps in due to change in environmental conditions such as temperature and humidity. In addition, aging of the sensors also introduce nonlinearity. Due to such nonlinearities direct digital readout is not possible. As a result the devices or sensors are used only in the linear region of their characteristics. In other words the usable range of these devices gets restricted due to nonlinearity problem. The accuracy of measurement is also affected if the full range of the instrument is used. The nonlinearity present in the characteristics is usually time-varying and unpredictable as it depends on many uncertain factors stated above. Hence the prime objective of the thesis is to study the nonlinearity problem associated with these devices and suggest novel methods of circumventing these effects by suitably designing intelligent systems. In the present investigation,..

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Exploring the use of neural network-based band selection on hyperspectral imagery to identify informative wavelengths for improving classifier task performance

Hyperspectral imagery is a highly dimensional type of data resulting in high computational costs during analysis. Band selection aims to reduce the original hyperspectral image to a smaller subset that reduces these costs while preserving the maximum amount of spectral information within the data. This thesis explores various types of band selection techniques used in hyperspectral image processing. Modifying Neural network-based techniques and observing the effects on the band selection process due to the change in network architecture or objective are of particular focus in this thesis. Herein, a generalized neural network-based band selection technique is developed and compared to state-of-the-art algorithms that are applied to a unique dataset and the Pavia City Center dataset where the subsequent selected bands are fed into a classifier to gather comparison results

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available