[PDF] Top 20 First-order logic learning in artificial neural networks
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First-order logic learning in artificial neural networks
... Abstract— Artificial Neural Networks have previously been applied in neuro-symbolic learning to learn ground logic pro- gram ...of learning relations using neuro-symbolic ...a ... See full document
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Overview of the Artificial Neural Networks and Fuzzy Logic Applications in Operational Hydrological Forecasting Systems
... In general, the fuzzy models used for hydrological forecasting are based on a fuzzy rule base describing the hydrological behaviour of the river basin. Exper t knowledge about specific discharge situations combined with ... See full document
6
Artificial Neural Networks in Financial Modelling
... adaptive learning behaviour as the environment ...such learning takes place, especially in how agents learn and ...an artificial financial market is fully developed, but today we are still far from ... See full document
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Pancreatic Cancer Detection and Diagnosis Expert System using Artificial Neural Networks and Fuzzy Logic Techniques
... that neural networks can make an accurate individualized prognosis of a patient given his or her particular ...using neural network provides a self-learning intelligent system that is capable ... See full document
5
Second Order Learning Algorithm for Back Propagation Neural Networks
... supervised learning method in the neural ...proposed first order and second order methods with adaptive gain (BP-AG, CGFR- AG, BFGS-AG) with standard second order methods without ... See full document
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Comparison of the performance of artificial neural networks and fuzzy logic for recognizing different partial discharge sources
... As first step, it is important to firstly identify the kind of defect through online PD classification looking at its PRPD ...in order to arrive at a more reliable ... See full document
20
Introduction to the Artificial Neural Networks
... In order to solve a given problem of supervised learning various steps has to be ...the first step we have to determine the type of training ...chosen artificial neural network. In the ... See full document
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Artificial Neural Networks A Review of Applications of Neural Networks in the Modeling of HIV Epidemic
... machine learning classifiers (MLCs) to seek differences in visual fields (VFs) between normal eyes and eyes of HIV postive patients in order to find the effect of immunodeficiency on VFs and to compare the ... See full document
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Brain-inspired replay for continual learning with artificial neural networks
... ples from a learned model of the world 62–64 . Regarding replay’s function, our findings highlight an important computational role for replay in incrementally learning new classes or categories. Being able to ... See full document
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Interpretable machine learning: Convolutional neural networks with RBF fuzzy logic classification rules
... The first few layers would be multiple pairs of convolution lay- ers and pooling ...deep networks, batch normalisation can also be applied in every layer ... See full document
8
Harnessing Deep Neural Networks with Logic Rules
... we first selected 5% training sentences uniformly at random, then trained the models on these sentences as well as their ...semi-supervised learning as in Rows 5-6, we get further improved ... See full document
11
Navigation with Artificial Neural Networks
... in order to build a theoretical backing for the roles of the ANNs and their ...inputs. First we shall explain the Kushner equation then breakout parts of the overall ... See full document
234
Fast relational learning using bottom clause propositionalization with artificial neural networks
... using neural networks and the ...in order to explore some of the capabilities of the approach: three versions of CILP++ trained with standard backpropagation, each one using three sizes of background ... See full document
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A. Artificial Neural Networks
... dynamic neural network and an innovative optimized adaptive unscented Kalman filter for forecasting stock price indices of four different Indian ...different learning strategies were used to adjust the ... See full document
7
Emulation Of Associative Learning in a Humanoid Robot using Artificial Neural Networks
... The main function of the Arduino is to read analog values and send them to the Rpi. This goal is to send these values in fewest number of bytes as possible, as frequently as possible. Failing which, the PID position ... See full document
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Diagnosing Knee Osteoarthritis Using Artificial Neural Networks and Deep Learning
... were first filtered using a second order Chebyshev filter which removed signals that were outside the bandwidth of the electromyography signals which roughly ranges from 20Hz to 500Hz ...deep ... See full document
8
Improved Learning Scheme for Cognitive Radio using Artificial Neural Networks
... in Neural Network Tool Box of MATLAB R2010 of Mathworks, Natick, MA, ...the neural network is done with stochastic data. Training in neural networks means the nonlinear mapping of input and ... See full document
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Stable Boundary Layer Height Parameterization: Learning from Artificial Neural Networks
... After the sample data have been collected, they need to be preprocessed and to be divided into three subsets be- fore they are used to train the network. The first subset is the training set, which is used for ... See full document
9
Neural Logic Reinforcement Learning
... reinforcement learning (DRL) has achieved significant breakthroughs in various ...deep neural networks makes the learned policies hard to be inter- ...named Neural Logic Reinforcement ... See full document
10
Forecasting Student Academic Performance by Decision Tree Learning Using Artificial Neural Networks
... in order to predict future academic performance ...the learning process, it itself is a complex phenomenon affected by many factors like the teaching environment and personal study ...active learning ... See full document
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