945 research outputs found
Approximation of the inverse kinematics of a robotic manipulator using a neural network
A fundamental property of a robotic manipulator system is that it is capable of accurately
following complex position trajectories in three-dimensional space. An essential component
of the robotic control system is the solution of the inverse kinematics problem which allows
determination of the joint angle trajectories from the desired trajectory in the Cartesian
space. There are several traditional methods based on the known geometry of robotic
manipulators to solve the inverse kinematics problem. These methods can become
impractical in a robot-vision control system where the environmental parameters can alter.
Artificial neural networks with their inherent learning ability can approximate the inverse
kinematics function and do not require any knowledge of the manipulator geometry.
This thesis concentrates on developing a practical solution using a radial basis function
network to approximate the inverse kinematics of a robot manipulator. This approach is
distinct from existing approaches as the centres of the hidden-layer units are regularly
distributed in the workspace, constrained training data is used and the training phase is
performed using either the strict interpolation or the least mean square algorithms. An
online retraining approach is also proposed to modify the network function approximation
to cope with the situation where the initial training and application environments are
different. Simulation results for two and three-link manipulators verify the approach.
A novel real-time visual measurement system, based on a video camera and image
processing software, has been developed to measure the position of the robotic manipulator
in the three-dimensional workspace. Practical experiments have been performed with a
Mitsubishi PA10-6CE manipulator and this visual measurement system. The performance
of the radial basis function network is analysed for the manipulator operating in two and
three-dimensional space and the practical results are compared to the simulation results.
Advantages and disadvantages of the proposed approach are discussed
PNNARMA model: an alternative to phenomenological models in chemical reactors
This paper is focused on the development of non-linear neural models able to provide appropriate predictions when acting as process simulators. Parallel identification models can be used for this purpose. However, in this work it is shown that since the parameters of parallel identification models are estimated using multilayer feed-forward networks, the approximation of dynamic systems could be not suitable. The solution proposed in this work consists of building up parallel models using a particular recurrent neural network. This network allows to identify the parameter sets of the parallel model in order to generate process simulators. Hence, it is possible to guarantee better dynamic predictions. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical reactor has been selected as a case study. The results suggest that parallel models based on the recurrent neural network proposed in this work can be seen as an alternative to phenomenological models for simulating the dynamic behaviour of the heating/cooling circuits.Publicad
Adaptive non linear system identification and channel equalization usinf functional link artificial neural network
In system theory, characterization and identification are fundamental problems. When the plant behavior is completely unknown, it may be characterized using certain model and then, its identification may be carried out with some artificial neural networks(ANN) like multilayer perceptron(MLP) or functional link artificial neural network(FLANN) using some learning rules such as back propagation (BP) algorithm. They offer flexibility, adaptability and versatility, so that a variety of approaches may be used to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. The primary aim of the present thesis is to provide a framework for the systematic design of adaptation laws for nonlinear system identification and channel equalization. While constructing an artificial neural network the designer is often faced with the problem of choosing a network of the right size for the task. The advantages of using a smaller neural network are cheaper cost of computation and better generalization ability. However, a network which is too small may never solve the problem, while a larger network may even have the advantage of a faster learning rate. Thus it makes sense to start with a large network and then reduce its size. For this reason a Genetic Algorithm (GA) based pruning strategy is reported. GA is based upon the process of natural selection and does not require error gradient statistics. As a consequence, a GA is able to find a global error minimum. Transmission bandwidth is one of the most precious resources in digital communication systems. Communication channels are usually modeled as band-limited linear finite impulse response (FIR) filters with low pass frequency response. When the amplitude and the envelope delay response are not constant within the bandwidth of the filter, the channel distorts the transmitted signal causing intersymbol interference (ISI). The addition of noise during propagation also degrades the quality of the received signal. All the signal processing methods used at the receiver's end to compensate the introduced channel distortion and recover the transmitted symbols are referred as channel equalization techniques.When the nonlinearity associated with the system or the channel is more the number of branches in FLANN increases even some cases give poor performance. To decrease the number of branches and increase the performance a two stage FLANN called cascaded FLANN (CFLANN) is proposed.This thesis presents a comprehensive study covering artificial neural network (ANN) implementation for nonlinear system identification and channel equalization. Three ANN structures, MLP, FLANN, CFLANN and their conventional gradient-descent training methods are extensively studied. Simulation results demonstrate that FLANN and CFLANN methods are directly applicable for a large class of nonlinear control systems and communication problems
Automated Determination of Stellar Parameters from Simulated Dispersed Images for DIVA
We have assessed how well stellar parameters (T_eff, logg and [Fe/H]) can be
retrieved from low-resolution dispersed images to be obtained by the DIVA
satellite. Although DIVA is primarily an all-sky astrometric mission, it will
also obtain spectrophotometric information for about 13 million stars
(operational limiting magnitude V ~ 13.5 mag). Constructional studies foresee a
grating system yielding a dispersion of ~200nm/mm on the focal plane (first
spectral order). For astrometric reasons there will be no cross dispersion
which results in the overlapping of the first to third diffraction orders. The
one-dimensional, position related intensity function is called a DISPI
(DISPersed Intensity). We simulated DISPIS from synthetic spectra (...) for a
limited range of metallicites i.e. our results are for [Fe/H] in the range -0.3
to 1 dex. We show that there is no need to deconvolve these low resolution
signals in order to obtain basic stellar parameters. Using neural network
methods and by including simulated data of DIVA's UV telescope, we can
determine T_eff to an average accuracy of about 2% for DISPIS from stars with
2000 K < T_eff < 20000 K and visual magnitudes of V=13 mag (end of mission
data). logg can be determined for all temperatures with an accuracy better than
0.25 dex for magnitudes brighter than V=12 mag. For low temperature stars with
2000 K < T_eff < 5000 K and for metallicities in the range -0.3 to +1 dex a
determination of [Fe/H] is possible (to better than 0.2 dex) for these
magnitudes. Additionally we examined the effects of extinction E(B-V) on DISPIS
and found that it can be determined to better than 0.07 mag for magnitudes
brighter than V=14 mag if the UV information is included.Comment: 12 pages, 8 figures, Accepted for publication in A&
Convolutional Neural Operators for robust and accurate learning of PDEs
Although very successfully used in conventional machine learning, convolution
based neural network architectures -- believed to be inconsistent in function
space -- have been largely ignored in the context of learning solution
operators of PDEs. Here, we present novel adaptations for convolutional neural
networks to demonstrate that they are indeed able to process functions as
inputs and outputs. The resulting architecture, termed as convolutional neural
operators (CNOs), is designed specifically to preserve its underlying
continuous nature, even when implemented in a discretized form on a computer.
We prove a universality theorem to show that CNOs can approximate operators
arising in PDEs to desired accuracy. CNOs are tested on a novel suite of
benchmarks, encompassing a diverse set of PDEs with possibly multi-scale
solutions and are observed to significantly outperform baselines, paving the
way for an alternative framework for robust and accurate operator learning
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