Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing
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Các bài báo tiêu biểu
Signal reconstruction from sampled data using neural network
- Trang 707-715
For reconstructing a signal from sampling data, the method based on Shannon's
sampling theorem is usually employed. The reconstruction error appears when the
signal does not satisfy the Nyquist condition. This paper proposes a new
reconstruction method by using a linear perceptron and multilayer perceptron as
FIR filter. The perceptron, which has weights obtained by learning when adapting
the orig...
#Signal reconstruction #Neural networks #Image reconstruction #Sampling methods #Finite impulse response filter #Frequency #Adaptive filters #Image sampling #Information retrieval #Physics
Neural network-based segmentation of textures using Gabor features
- Trang 365-374
The effectiveness of Gabor filters for texture segmentation is well known. In
this paper, we propose a texture identification scheme, based on a neural
network (NN) using Gabor features. The features are derived from both the Gabor
cosine and sine filters. Through experiments, we demonstrate the effectiveness
of a NN based classifier using Gabor features for identifying textures in a
controlled en...
#Neural networks #Gabor filters #Frequency #Multilayer perceptrons #Multi-layer neural network #Image segmentation #Classification algorithms #Clustering algorithms #Robustness #Satellites
Adaptive BP neural network (ABPNN) based PN code acquisition system via recursive accumulator
- Trang 737-745
An adaptive back propagation (BP) neural network based PN code acquisition
system is presented. Conventional neural network based acquisition systems are
usually trained on PN code, but this system is based on training a back
propagation neural network at all possible phases of the output of a correlation
detector which is modified by a recursive accumulator. The recursive accumulator
can converge...
#Adaptive systems #Neural networks #Signal to noise ratio #Phase detection #Detectors #Control systems #Training data #Additive white noise #Gaussian noise #Computer simulation
Unsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data
- Trang 77-86
This manuscript describes a combined approach of unsupervised clustering
followed by supervised learning that provides an efficient classification of
conditions in DNA array gene expression experiments (different cell lines
including some cancer types, in the cases shown). Firstly the dimensionality of
the dataset of gene expression profiles is reduced to a number of non-redundant
clusters of co-e...
#Supervised learning #Gene expression #DNA #Cancer #Neural networks #Principal component analysis #Clustering algorithms #Support vector machines #Support vector machine classification #Biotechnology
Parallel and separable recursive Levenberg-Marquardt training algorithm
- Trang 129-138
A novel decomposed recursive Levenberg Marquardt (RLM) algorithm is derived for
the training of feedforward neural networks. By neglecting interneuron weight
correlations the recently proposed RLM training algorithm can be decomposed at
neuron level enabling weights to be updated in an efficient parallel manner. A
separable least squares implementation of decomposed RLM is also introduced.
Experim...
#Neurons #Cost function #Neural networks #Least squares methods #Convergence #Partitioning algorithms #Feedforward neural networks #Training data #Backpropagation algorithms #Resonance light scattering
Classification and ICA using maximum likelihood Hebbian learning
- Trang 327-336
We investigate an extension of Hebbian learning in a principal component
analysis network which has been derived to be optimal for a specific probability
density function(PDF). We note that this probability density function is one of
a family of PDFs and investigate the learning rules formed in order to be
optimal for several members of this family. We show that, whereas previous
authors have view...
#Independent component analysis #Hebbian theory #Principal component analysis #Neurons #Artificial neural networks #Negative feedback #Nonlinear equations #Computational intelligence #Probability density function #Mean square error methods
Towards a tunable tactile communication system: concept and first experiments
- Trang 767-776
We present a novel concept of a tactile communication system with dialog-based
tuning possibilities for the exploration of tactile language developments. An
experimental implementation of the proposed tactile intelligent sensory
substitution system (TIS/sup 3/) is being tested in a closed loop set up with
human subjects. TIS/sup 3/ consists of a tactile encoder (TE) to map desired
objects onto a p...
#Humans #Tellurium #Intelligent systems #Intelligent sensors #Skin #Signal generators #Computer science #System testing #Information processing #Digital signal processing
Ký ức liên kết tạm thời và sự xấp xỉ hàm với bản đồ tự tổ chức Dịch bởi AI
- Trang 109-118
Chúng tôi đề xuất một kỹ thuật lập mô hình nơ-ron không giám sát, gọi là ký ức
liên kết tạm thời dạng vector (VQTAM), cho phép bản đồ tự tổ chức của Kohonen
(SOM) xấp xỉ các ánh xạ động lực phi tuyến trên toàn cầu. Phân tích lý thuyết về
phương pháp VQTAM cho thấy rằng độ sai lệch xấp xỉ giảm đi khi quá trình đào tạo
SOM diễn ra. SOM được so sánh với mạng MLP tiêu chuẩn và mạng RBF trong việc xác
...
#Ký ức liên kết #Xấp xỉ hàm #Mô hình hóa hệ thống sinh học #Thiết bị truyền động thủy lực #Hệ thống động lực phi tuyến #Mô hình toán học #Mô hình dự đoán #Hệ thống điều khiển phi tuyến #Mạng hàm cơ sở bán kính #Roentgenium
Finding temporal structure in music: blues improvisation with LSTM recurrent networks
- Trang 747-756
We consider the problem of extracting essential ingredients of music signals,
such as a well-defined global temporal structure in the form of nested
periodicities (or meter). We investigate whether we can construct an adaptive
signal processing device that learns by example how to generate new instances of
a given musical style. Because recurrent neural networks (RNNs) can, in
principle, learn the...
#Intelligent networks #Multiple signal classification #Recurrent neural networks #Timing #Adaptive signal processing #Signal generators #Signal processing #Machine learning #Bars #Learning systems
Detection of unusual human behavior in intelligent house
- Trang 697-706
This paper describes a model, based on a Markov process model, of daily human
behavior in an intelligent house where human behavior is observed with small
motion detectors. The number of sensor states is reduced to a few dozen by a
vector quantization method, and transitions within this reduced set of states
are observed. Then, the state transition probability and the transition duration
time dist...
#Humans #Intelligent sensors #Home appliances #Vector quantization #Infrared detectors #Motion detection #Programming #Computer industry #Research and development #Cities and towns