Feed Forward Propagation Network Research Articles

Normalized Min-Sum Neural Network for LDPC Decoding

The success of deep learning has encouraged its applications in decoding error-correcting codes, e.g., LDPC decoding. In this paper, we propose a model-driven deep learning method for normalized min-sum (NMS) low-density parity-check (LDPC) decoding, namely neural NMS (NNMS) LDPC decoding network. By unfolding the iterative decoding progress between checking nodes (CNs) and variable nodes (VNs) into a feed-forward propagation network, we can harvest the benefits of both the model-driven deep learning and the conventional normalized min-sum (NMS) LDPC decoding method. In addition, we proposed a shared parameters NNMS with the LeakyReLU and a 12-bit quantizer (SNNMS-LR-Q) which reduces the number of required multipliers and correction factors by sharing parameters, increasing the nonlinear fitting ability by adding LeakyReLU. By utilizing the 12-bit quantizer, we can improve the confrontation ability. Thorough experiments with different code lengths, code rates, channel conditions, and check matrices are implemented to demonstrate the advantages and robustness of our proposed networks. The BER performance of the proposed NNMS is 1.5 dB better than the NMS, using fewer iterations. Meanwhile, The SNNMS-LR-Q outperforms the NNMS regarding the BER performance and efficiency.

Efficiency of artificial neural networks in determining scour depth at composite bridge piers

ABSTRACT Scouring is the most common cause of bridge failure. This study was conducted to evaluate the efficiency of the Artificial Neural Networks (ANN) in determining scour depth around composite bridge piers. The experimental data, attained in different conditions and various pile cap locations, were used to obtain the ANN model and to compare the results of the model with most well-known empirical, HEC-18 and FDOT, methods. The data were divided into training and evaluation sets. The ANN models were trained using the experimental data, and their efficiency was evaluated using statistical test. The results showed that to estimate scour at the composite piers, feed-forward propagation network with three neurons in the hidden layer and hyperbolic sigmoid tangent transfer function was with the highest accuracy. The results also indicated a better estimation of the scour depth by the proposed ANN than the empirical methods.

Deep Learning Through Convolutional Neural Networks

Deep learning which is associated with the basics of Machine Learning has become popular over the years because of its fast paced adaptability and ability to handle complex problems. Prior to this technology breakthrough traditional methods of machine learning were used in applications of Image processing and pattern recognition, and analytics .With the advent of CNNs it has become easy to combat complex learning problems using the property of specificity and accuracy in CNN architectures and methodologies. This paper gives an introductory insights in CNNs like the feed-forward propagation networks and Back propagation Networks. The paper explains steps followed by CNNs for classifying the input and generating a predefined output. It also explains evolution of multiple Image CNN architectures which find applications in multiple domains of Computer Science like Image Processing & Segmentation, Pattern Recognition & Predictive Analytics, Text Analytics to name a few .

ADAPTIVE NEURAL NETWORK FOR SKETCH BASED IMAGE RETRIEVAL

In this paper, we present neural network approach for Sketch Based Image Retrieval (SBIR) using Histogram of Gradient (HOG) feature descriptor. This paper emphasis on back propagation Feed-forward Network (FFN) and Pattern Recognition Network (PRN) used for sketch based retrieval. Neural network is a popular tool used for pattern recognition and approximation of unknown nonlinear functions. We use features descriptor like Histogram of Gradient (HOG) for free hand and human face sketches and these features are used to training of network. Experimental results and analysis are based on CHUK and Flicker dataset, used for training and testing. Different similarity measure functions are discussed and used similarity between query by non-expert sketchers and database.