Ear recognition with feed-forward artificial neural networks

Biometrics has become one of the most important techniques in recognizing a person’s identity. A person’s face, iris and fingerprint are mostly used in biometrics today. It has been established that there are no two ears exactly alike, even in the cases of identical twins. In this paper, we define a 7-element ear feature set and design and train a feed-forward artificial neural network to recognize a human ear. We train and test the network with 51 ear pictures from 51 different persons. Simulation experiments with various networks with various number of layers and number of neurons per layer and with and without noise are conducted. Results indicate that a 95 % ear recognition accuracy is achieved with a simple 3-layer feed-forward neural network with only a total of 18 neurons even in the presence of some noise. This design outstands previous work in simplicity and implementation cost.     

Speech recognition using pulse-coupled neural networks with a radial basis function

We have developed a novel pulse-coupled neural network (PCNN) for speech recognition. One of the advantages of the PCNN is in its biologically based neural dynamic structure using feedback connections. To recall the memorized pattern, a radial basis function (RBF) is incorporated into the proposed PCNN. Simulation results show that the PCNN with a RBF can be useful for phoneme recognition. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

American Sign Language word recognition with a sensory glove using artificial neural networks

An American Sign Language (ASL) recognition system is being developed using artificial neural networks (ANNs) to translate ASL words into English. The system uses a sensory glove called the Cyberglove™ and a Flock of Birds^® 3-D motion tracker to extract the gesture features. The data regarding finger joint angles obtained from strain gauges in the sensory glove define the hand shape, while the data from the tracker describe the trajectory of hand movements. The data from these devices are processed by a velocity network with noise reduction and feature extraction and by a word recognition network. Some global and local features are extracted for each ASL word. A neural network is used as a classifier of this feature vector. Our goal is to continuously recognize ASL signs using these devices in real time. We trained and tested the ANN model for 50 ASL words with a different number of samples for every word. The test results show that our feature vector extraction method and neural networks can be used successfully for isolated word recognition. This system is flexible and open for future extension.     

Compartmental modelling with artificial neural networks

In this paper, we introduce an abbreviated compartmental modelling scheme which may be of interest to those in neuron- based adaptive systems because of the additional scope it provides for studying biologically-inspired learning mechanisms. The scheme, although not as flexible and precise as the general compartmental approach, allows one to design Hodgkin-Huxley style cells, and passive dendritic trees with an arbitrary number of synaptic connections. The trade-offs made for computational performance, may make the modelling scheme more appropriate for practical applications. The modelling scheme is based upon artificial neural networks, which we have used to represent cylindrical compartments (both passive and active) of different lengths, two types of voltage-dependent channels, and basic chemical synapses with variable time constants.     

Rule-based artificial neural networks

Neural networks that are integrated with rule-based systems having a knowledge base offer more capabilities than networks not integrated with such systems.     

Knowledge-based artificial neural networks

Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for accurately classifying examples not seen during training. The challenge of hybrid learning systems is to use the information provided by one source of information to offset information missing from the other source. By so doing, a hybrid learning system should learn more effectively than systems that use only one of the information sources. KBANN (Knowledge-Based Artificial Neural Networks) is a hybrid learning system built on top of connectionist learning techniques. It maps problem-specific “domain theories”, represented in propositional logic, into neural networks and then refines this reformulated knowledge using backpropagation. KBANN is evaluated by extensive empirical tests on two problems from molecular biology. Among other results, these tests show that the networks created by KBANN generalize better than a wide variety of learning systems, as well as several techniques proposed by biologists.     

Atrial fibrillation classification with artificial neural networks

The acquired 72 normal sinus rhythm ECGs and 80 ECGs with atrial fibrillation (AF) are decomposed with ‘db10’ Daebauchies wavelets at level 6 and power spectral density was calculated for each decomposed signal with Welch method. Average power spectral density was calculated for six subbands and normalized to be used as input to the neural network. Levenberg-Marquart backpropagation feed forward neural network was built from logarithmic sigmoid transfer functions in three-layer form. The trained network was tested on 24 normal and 28 AF state ECGs. The classification performance was accomplished as 100% accurate.     

Integrating artificial neural networks with rule-based expert systems

The Rule-Based (RB) and the Artificial Neural Network (ANN) approaches to expert systems development have each demonstrated some specific advantages and disadvantages. These two approaches can be integrated to exploit the advantages and minimize the disadvantages of each method used alone. An RB/ANN integrated approach is proposed to facilitate the development of an expert system which provides a “high-performance” knowledge-based network, an explanation facility, and an input/output facility. In this case study an expert system designed to assist managers in forecasting the performance of stock prices is developed to demonstrate the advantages of this integrated approach and how it can enhance support for managerial decision making.     

Controlling fast spring-legged locomotion with artificial neural networks

Controlling the model of an one-legged robot is investigated. The model consists merely of a mass less spring attached to a point mass. The motion of this system is characterised by repeated changes between ground contact and flight phases. It can be kept in motion by active control only. Robots that are suited for fast legged locomotion require different hardware layouts and control approaches in contrast to slow moving ones. The spring mass system is a simple model that describes this principle movement of a spring-legged robot. Multi-Layer-Perceptrons (MLPs), Radial Basis Functions (RBFs) and Self-Organising Motoric Maps (SOMMs) were used to implement neurocontrollers for such a movement system. They all prove to be suitable for control of the movement. This is also shown by an experiment where the environment of the spring-mass system is changed from even to uneven ground. The neurocontroller is performing well with this additional complexity without being trained for it.     

Clustering with artificial neural networks and traditional techniques

In this article, two clustering techniques based on neural networks are introduced. The two neural network models are the Harmony theory network (HTN) and the self‐organizing logic neural network (SOLNN), both of which are characterized by parallel processing, a distributed architecture, and a large number of nodes. After describing their clustering characteristics and potential, a comparison to classical statistical techniques is performed. This comparison allows the creation of a correspondence between each neural network clustering technique and particular metrics as used by the corresponding statistical methods, which reflect the affinity of the clustered patterns. In particular, the HTN is found to perform the clustering task with an accuracy similar to the best statistical methods, while it is further capable of proposing an optimal number of groups into which the patterns may be clustered. On the other hand, the SOLNN combines a high clustering accuracy with the ability to cluster higher‐dimensional patterns without a considerable increase in the processing time. © 2003 Wiley Periodicals, Inc.     

Handwritten digit recognition by neural networks with single-layertraining

It is shown that neural network classifiers with single-layer training can be applied efficiently to complex real-world classification problems such as the recognition of handwritten digits. The STEPNET procedure, which decomposes the problem into simpler subproblems which can be solved by linear separators, is introduced. Provided appropriate data representations and learning rules are used, performance comparable to that obtained by more complex networks can be achieved. Results from two different databases are presented: an European database comprising 8700 isolated digits and a zip code database from the US Postal Service comprising 9000 segmented digits. A hardware implementation of the classifier is briefly described.     

卷积神经网络在验证码识别上的应用与研究

针对日前在互联网中被广泛使用的验证码,提出将卷积神经网络引入到验证码识别之中。通过设计新的卷积网络拓扑结构,针对可分割成语验证码提出基于k-means聚类的字符分割方法,以及仿真验证码图片自生成训练集,通过预训练过程使得卷积训练网络具有良好初值,加速收敛并提高模型泛化能力;针对不可分割验证码,省去字符分割操作直接将验证码整体作为卷积模型输入,引入仿射变换、水漫填充等预处理和SVM算法二分类,或者结合多标记学习方法设计卷积拓扑等。最终实验研究证实卷积神经网络对于可分割及不可分割验证码的平均识别准确率均收敛于99%左右。     

Simulation metamodeling through artificial neural networks

Simulation metamodeling has been a major research field during the last decade. The main objective has been to provide robust, fast decision support aids to enhance the overall effectiveness of decision-making processes. This paper discusses the importance of simulation metamodeling through artificial neural networks (ANNs), and provides general guidelines for the development of ANN-based simulation metamodels. Such guidelines were successfully applied in the development of two ANNs trained to estimate the manufacturing lead times (MLT) for orders simultaneously processed in a four-machine job shop.The design of intelligent systems such as ANNs may help to avoid some of the drawbacks of traditional computer simulation. Metamodels offer significant advantages regarding time consumption and simplicity to evaluate multi-criteria situations. Their operation is notoriously fast compared to the time required to operate conventional simulation packages.     

ULSI architectures for artificial neural networks

No clear consensus exists about how to exploit the potential for massively parallel implementations of artificial neural networks. Three hardware implementations to demonstrate the key issues surrounding their use are: model specific integrated circuits for neural associative memories, self-organizing feature maps, and local cluster neural networks     

VLSI technologies for artificial neural networks

VLSI systems, basic integrated circuits, and silicon technologies are discussed. Novel circuit and design principles that provide a foundation for the implementation of a wide variety of neural network models in silicon are described. The key issues for a successful integration of neural systems are identified. The realization of algorithms in silicon is examined. Special-purpose hardware for carrying out the activation and transfer function and for the connection elements is discussed. A brief overview of the current silicon technologies is provided     

Intelligent-memory architecture for artificial neural networks

Execution of artificial neural networks, especially for online pattern recognition, mainly depends on time-efficient execution of weighted sums. A new architecture achieves this goal, with a computation time superior to the time complexity of sequential von Neumann machines. This architecture uses additional logic to extend the functionality of conventional RAM. The authors discuss an implementation of this architecture that uses reconfigurable logic     

Symbolic interpretation of artificial neural networks

Hybrid intelligent systems that combine knowledge-based and artificial neural network systems typically have four phases, involving domain knowledge representation, mapping of this knowledge into an initial connectionist architecture, network training and rule extraction, respectively. The final phase is important because it can provide a trained connectionist architecture with explanation power and validate its output decisions. Moreover, it can be used to refine and maintain the initial knowledge acquired from domain experts. In this paper, we present three rule extraction techniques. The first technique extracts a set of binary rules from any type of neural network. The other two techniques are specific to feedforward networks, with a single hidden layer of sigmoidal units. Technique 2 extracts partial rules that represent the most important embedded knowledge with an adjustable level of detail, while the third technique provides a more comprehensive and universal approach. A rule-evaluation technique, which orders extracted rules based on three performance measures, is then proposed. The three techniques area applied to the iris and breast cancer data sets. The extracted rules are evaluated qualitatively and quantitatively, and are compared with those obtained by other approaches     

Structured neural networks for pattern recognition

This paper proposes a novel approach for the design of structures of neural networks for pattern recognition. The basic idea lies in subdividing the whole classification problem in smaller and simpler problems at different levels, each managed by appropriate components of a complex neural architecture. Three neural structures are presented and applied in a surveillance system aimed at monitoring a railway waiting room classifying potential dangerous situations. Each architecture is composed by nodes, which are actual multilayer perceptrons trained to discriminate between subsets of classes until a complete separation among the classes is achieved. This approach showed better performances with respect to a classical statistical classification procedures and to a single neural network.