15 research outputs found
Isotropization of Quaternion-Neural-Network-Based PolSAR Adaptive Land Classification in Poincare-Sphere Parameter Space
Quaternion neural networks (QNNs) achieve high accuracy in polarimetric synthetic aperture radar classification for various observation data by working in Poincare-sphere-parameter space. The high performance arises from the good generalization characteristics realized by a QNN as 3-D rotation as well as amplification/attenuation, which is in good consistency with the isotropy in the polarization-state representation it deals with. However, there are still two anisotropic factors so far which lead to a classification capability degraded from its ideal performance. In this letter, we propose an isotropic variation vector and an isotropic activation function to improve the classification ability. Experiments demonstrate the enhancement of the QNN ability
Quaternion Neuro-Fuzzy Learning Algorithm for Fuzzy Rule Generation
Abstract—In order to generate or tune fuzzy rules, Neuro-
Fuzzy learning algorithms with Gaussian type membership
functions based on gradient-descent method are well known.
In this paper, we propose a new learning approach, the
Quaternion Neuro-Fuzzy learning algorithm. This method is
an extension of the conventional method to four-dimensional
space by using a quaternion neural network that maps
quaternion to real values. Input, antecedent membership
functions and consequent singletons are quaternion, and
output is real. Four-dimensional input can be better
represented by quaternion than by real values. We compared
it with the conventional method by several function
identification problems, and revealed that the proposed
method outperformed the counterpart: The number of rules
was reduced to 5 from 625, the number of epochs by one
fortieth, and error by one tenth in the best cases.The Second International Conference on Robot, Vision and Signal Processing
December 10-12, 2013 Kitakyushu, Japa
Personal Identification Using Ultrawideband Radar Measurement of Walking and Sitting Motions and a Convolutional Neural Network
This study proposes a personal identification technique that applies machine
learning with a two-layered convolutional neural network to spectrogram images
obtained from radar echoes of a target person in motion. The walking and
sitting motions of six participants were measured using an ultrawideband radar
system. Time-frequency analysis was applied to the radar signal to generate
spectrogram images containing the micro-Doppler components associated with limb
movements. A convolutional neural network was trained using the spectrogram
images with personal labels to achieve radar-based personal identification. The
personal identification accuracies were evaluated experimentally to demonstrate
the effectiveness of the proposed technique.Comment: 9 pages, 7 figures, and 3 table
Quaternion Backpropagation
Quaternion valued neural networks experienced rising popularity and interest
from researchers in the last years, whereby the derivatives with respect to
quaternions needed for optimization are calculated as the sum of the partial
derivatives with respect to the real and imaginary parts. However, we can show
that product- and chain-rule does not hold with this approach. We solve this by
employing the GHRCalculus and derive quaternion backpropagation based on this.
Furthermore, we experimentally prove the functionality of the derived
quaternion backpropagation
A Quaternion Gated Recurrent Unit Neural Network for Sensor Fusion
Recurrent Neural Networks (RNNs) are known for their ability to learn relationships within temporal sequences. Gated Recurrent Unit (GRU) networks have found use in challenging time-dependent applications such as Natural Language Processing (NLP), financial analysis and sensor fusion due to their capability to cope with the vanishing gradient problem. GRUs are also known to be more computationally efficient than their variant, the Long Short-Term Memory neural network (LSTM), due to their less complex structure and as such, are more suitable for applications requiring more efficient management of computational resources. Many of such applications require a stronger mapping of their features to further enhance the prediction accuracy. A novel Quaternion Gated Recurrent Unit (QGRU) is proposed in this paper, which leverages the internal and external dependencies within the quaternion algebra to map correlations within and across multidimensional features. The QGRU can be used to efficiently capture the inter- and intra-dependencies within multidimensional features unlike the GRU, which only captures the dependencies within the sequence. Furthermore, the performance of the proposed method is evaluated on a sensor fusion problem involving navigation in Global Navigation Satellite System (GNSS) deprived environments as well as a human activity recognition problem. The results obtained show that the QGRU produces competitive results with almost 3.7 times fewer parameters compared to the GRU
Adaptive land classification and new class generation by unsupervised double-stage learning in Poincare sphere space for polarimetric synthetic aperture radars
Polarimetric satellite-borne synthetic aperture radar (PolSAR) is expected to provide land usage information globally and precisely. In this paper, we propose a unsupervised double-stage learning land state classification system using a self-organizing map (SOM) that utilizes ensemble variation vectors. We find that the Poincare sphere parameters representing the polarization state of scattered wave have specific features of the land state, in particular, in their ensemble variation rather than spatial variation. Experiments demonstrate that the proposed PolSAR double-stage SOM system generate new classes appropriately, resulting in successful fine land classification and/or appropriate new class generation