Search results for "feedforward neural network"
showing 9 items of 19 documents
Hardware implementation of real-time Extreme Learning Machine in FPGA: Analysis of precision, resource occupation and performance
2016
Extreme Learning Machine (ELM) on-chip learning is implemented on FPGA.Three hardware architectures are evaluated.Parametrical analysis of accuracy, resource occupation and performance is carried out. Display Omitted Extreme Learning Machine (ELM) proposes a non-iterative training method for Single Layer Feedforward Neural Networks that provides an effective solution for classification and prediction problems. Its hardware implementation is an important step towards fast, accurate and reconfigurable embedded systems based on neural networks, allowing to extend the range of applications where neural networks can be used, especially where frequent and fast training, or even real-time training…
A Novel Systolic Parallel Hardware Architecture for the FPGA Acceleration of Feedforward Neural Networks
2019
New chips for machine learning applications appear, they are tuned for a specific topology, being efficient by using highly parallel designs at the cost of high power or large complex devices. However, the computational demands of deep neural networks require flexible and efficient hardware architectures able to fit different applications, neural network types, number of inputs, outputs, layers, and units in each layer, making the migration from software to hardware easy. This paper describes novel hardware implementing any feedforward neural network (FFNN): multilayer perceptron, autoencoder, and logistic regression. The architecture admits an arbitrary input and output number, units in la…
Data-based modeling of vehicle collisions by nonlinear autoregressive model and feedforward neural network
2013
Vehicle crash test is the most direct and common method to assess vehicle crashworthiness. Visual inspection and obtained measurements, such as car acceleration, are used, e.g. to examine impact severity of an occupant or to assess overall car safety. However, those experiments are complex, time-consuming, and expensive. We propose a method to reproduce car kinematics during a collision using nonlinear autoregressive (NAR) model which parameters are estimated by the use of feedforward neural network. NAR model presented in this study is derived from the more general one - nonlinear autoregressive with moving average (NARMA). Suitability of autoregressive systems for data-based modeling was …
The CALMA system: an artificial neural network method for detecting masses and microcalcifications in digitized mammograms
2004
The CALMA (Computer Assisted Library for MAmmography) project is a five years plan developed in a physics research frame in collaboration between INFN (Istituto Nazionale di Fisica Nucleare) and many Italian hospitals. At present a large database of digitized mammographic images (more than 6000) was collected and a software based on neural network algorithms for the search of suspicious breast lesions was developed. Two tools are available: a microcalcification clusters hunter, based on supervised and unsupervised feedforward neural network, and a massive lesions searcher, based on a hibrid approach. Both the algorithms analyzed preprocessed digitized images by high frequency filters. Clini…
Forecasting daily urban electric load profiles using artificial neural networks
2004
The paper illustrates a combined approach based on unsupervised and supervised neural networks for the electric energy demand forecasting of a suburban area with a prediction time of 24 h. A preventive classification of the historical load data is performed during the unsupervised stage by means of a Kohonen's self organizing map (SOM). The actual forecast is obtained using a two layered feed forward neural network, trained with the back propagation with momentum learning algorithm. In order to investigate the influence of climate variability on the electricity consumption, the neural network is trained using weather data (temperature, relative humidity, global solar radiation) along with h…
Unknown order process emulation
2002
Approaches the emulation problem using feedforward neural networks of single input single output (SISO) processes, applying a backpropagation method with a higher convergence rate. In this kind of application, difficult problems appear when the system's order is a priori unknown. A search through the SISO processes space is proposed, aiming to find a favorable neural emulator over the training examples set.
A Neuro-Genetic Approach to Real-Time Visual Grasp Synthesis
2007
Grasping is an essential prerequisite for an agent, either human or robotic, to manipulate various kinds of objects present in the world. It is a fact that we would like robots to have the same skills as we do. However, despite the construction of human-hand-like robotic effectors, much work is still to be done in order to give robots the capability to grasp and manipulate objects. The goal of this work is to automatically perform grasp synthesis of unknown planar objects. In other words, we must compute points on the object's boundary to be reached by the robotic fingers such that the resulting grasp, among infinite possibilities, optimizes some given criteria. The space of possible config…
Investigation of vehicle crash modeling techniques: theory and application
2013
Published version of an article in the journal: The International Journal of Advanced Manufacturing Technology. Also available from the publisher at: http://dx.doi.org/10.1007/s00170-013-5320-3 Creating a mathematical model of a vehicle crash is a task which involves considerations and analysis of different areas which need to be addressed because of the mathematical complexity of a crash event representation. Therefore, to simplify the analysis and enhance the modeling process, in this work, a brief overview of different vehicle crash modeling methodologies is proposed. The acceleration of a colliding vehicle is measured in its center of gravity—this crash pulse contains detailed informati…
Reproduction of kinematics of cars involved in crash events using nonlinear autoregressive models
2012
Vehicle crashworthiness can be assessed by the variety of methods - the most common and direct one is a vehicle crash test. Visual inspection and obtained measurements, such as car acceleration, are used to examine impact severity of an occupant and overall car safety. However, those experiments are complex, time-consuming, and expensive. We propose a method to reproduce car kinematics during a collision using a feedforward neural network to estimate the system by use of nonlinear autoregressive (NAR) models. Specifically, feasibility of applying neural networks with an NAR model to the analysis of experimental data is explored by application to measurements of a vehicle crash test. This mo…