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There is a great interest in the Genetic Programming (GP) community to develop semantic genetic operators. Most recent approaches includes the genetic programming framework for symbolic regression called Error Space Alignment GP, the geometric semantic operators, and our previous work the semantic crossover based on the partial derivative error. To the best of our knowledge, there has not been a semantic...
Electric energy plays a vital role in the achievement of social, economic and environment development of any nation. Thus, efficient demand planning and production of energy is needed to avoid too much over/under-estimation of electric load. In this study, the researchers proposed a scheme with eight steps for a dynamic time series forecasting using adaptive multilayer perceptron with minimal complexity...
This paper presents a program which identifies the 4-panel LCC equivalent of rice plants using image processing techniques and pattern recognition of the Backpropagation neural network. Images of the fully expanded healthy leaves were captured by digital camera and processed through RGB acquisition, color transformation, image enhancement, image segmentation and feature extraction procedures. The...
Complex-Valued Neural Networks (CVNNs) are Artificial Neural Networks (ANNs) which function using complex numbers - they have complex-valued parameters and accept complex-valued inputs. Phase-Based Neurons (PBNs) are simple CVNNs that use for the internal weights complex numbers with the modulus 1, the only adaptable parameters being the phases of the weights. We present in this paper some limitations...
This paper deals with time series exponential smoothing and artificial neural network-based backpropagation methods in formulating a reservoir inflow forecasting model for Angat Dam in the Philippines. The predictive model is trained using dam daily average inflow observations inclusive of years 2003 to 2012, as recorded. Any real-time inflows forming a 5-consecutive-day vector could serve as input...
Feedforward deep neural networks that employ multiple hidden layers show high performance in many applications, but they demand complex hardware for implementation. The hardware complexity can be much lowered by minimizing the word-length of weights and signals, but direct quantization for fixed-point network design does not yield good results. We optimize the fixed-point design by employing backpropagation...
Because of the complex dynamic behavior of supercapacitor, its modeling must be based on parallel, distributed structures (each component has to represent a model of activity, distributed on many processing units), with learning capacity. For this purpose, the paper proposes a new feed forward artificial neural network structure with two hidden layers and with backpropagation training. The neural...
Rising admissions in the South African institutions of higher education have enlarged student-to-lecturer ratios and increased the lecturer's workload, already burdened by administrative tasks. After marking tests, lecturers usually fill in a document called the cover page where the student's number, name and marks according to the questions are placed. Once this is done, they will have to recopy...
Batik is an Indonesian's traditional cloth which has been recognized as one of the world cultural heritage. Currently, there are hundreds of different batik motif which can be classified into 7 groups, i.e. Parang, Ceplok, Lereng, Megamendung, Semen, Lunglungan, and Buketan. This research develops a software to automatically identify motifs of batik image using color-texture-based feature extraction...
Electroencephalogram (EEG) data analysis algorithms consist of multiple processing steps each with a number of free parameters. A joint optimization methodology can be used as a wrapper to fine-tune these parameters for the patient or application. This approach is inspired by deep learning neural network models, but differs because the processing layers for EEG are heterogeneous with different approaches...
Presently, all over the world enormous amount of investment are dealing by the Stock Markets. Nationwide financial system are sturdily connected and closely inclined to the accomplishment of their Stock Markets. Additionally nowadays trading has become too reachable capital expenditure medium, for both planned investors as well as common man also. Artificial neural networks (ANN), belonging to Artificial...
There has been wide range of expansion in hand gestures close in style, pattern, movement and feature as well as in meaning. From that aspect this following paper confabs an improved version of hand gesture recognition based on segmentation process and backpropagation algorithm through the analysis of myoelectric signals generates from the movement of brachioradialis muscle and antebrachial vein of...
We propose a novel biologically plausible actor-critic algorithm using policy gradients in order to achieve practical, model-free reinforcement learning. It does not rely on backpropagation and is the first neural actor-critic relying only on locally available information. We show it has an advantage over pure policy gradients methods for motor learning performance in the polecart problem. We are...
There has been developed many method for the better convergence and generalization ability of neural network. Multilayer Perceptron (MLP) is made multi hidden layered structure for better performance. But in these types of structures still error from any output classes propagates in the backward direction which has a negative impact on the weight updating as well as overall performance because every...
The usage of IEEE 802.15.4, Wireless Sensor Networks in our daily life has shown exponential growth in the past decade. Localization of wireless sensor networks is the most critical aspects of this network. One of the major models used in localization, uses Multilayered Perceptron for training its data. This paper focuses on the impact of various MLP training functions on range based localization...
Handwritten Bangla digit recognition is one of the most attractive area for researchers who have interest in image processing and pattern recognition field. In our everyday activities like bank check identification, passport and document analysis, number plate identification and especially in our postal automation service, recognition of handwritten digits plays a significant role. That's why a rich...
License plate detection and character recognition have unveiled new possibilities and challenges in the field of intelligent transport system. Numerous algorithms have been proposed regarding license plate localization and tracking, character segmentation and character recognition. License plate character recognition is still an active area of research specially in terms of processing complexity....
An adjusted empirical mode decomposition method, built on Student's probability density function is presented. Compared to the original EMD, the new version provides a lower number of intrinsic mode functions and is more accurate in signal modeling and prediction. Using a backpropagation neural network for learning and in-sample prediction, our experimental results on a synthetic signal, an electrocardiogram...
Recognition of Bangla handwritten characters is a difficult but important task for various emerging applications. For better recognition performance, good feature representation of the character images is a primary requirement. In this study, we investigate a recently proposed machine learning approach called deep learning [1] for Bangla hand written character recognition, with a focus on automatic...
The paper proposes a novelty detector based on an artificial neural network forecaster. It shows how such forecaster can be constructed and as a novelty detector. Two variations of the forecaster are presented — one is based on backpropagation, and the other on Rprop. It is shown how the detector can be used to approach the exploration vs. exploitation trade-off. Experimental results are presented...
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