Neural networks are an information-processing paradigm inspired by the way the densely interconnected, parallel structure of the mammalian brain processes information. Artificial neural networks are collections of mathematical models that emulate some of the observed properties of biological nervous systems and draw on the analogies of adaptive biological learning. The key element of the ANN paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements that are analogous to neurons and are tied together with weighted connections that are analogous to synapses.1
The following are three ways that Fuzzy Min-Max Networks are being utilized:
Discriminating Between Benign and Malignant Gastric Lesions
A team at the National Technical University of Athens has developed a diagnostic system that employs morphometry combined with a fuzzy neural network approach to discriminate between benign and malignant gastric lesions. The input to the system consists of images of routine processed gastric smears stained by Papanicolaou technique. The analysis of the images provides a data set of cell features. The fuzzy min-max neural network classifier, an efficient pattern recognition approach, is used to classify benign and malignant cells based on the extracted morphometric and textural features. The network is based on hyberbox fuzzy sets and can be incrementally trained, requiring only one pass through the training set. The application of the fuzzy min-max neural network yields high rates of correct classification at both the cell level and the patient level. These results indicate that the use of intelligent computational techniques along with image morphometry can provide useful information about the potential of malignancy of gastric cells.2
Color Face Segmentation Using a Fuzzy Min-Max Neural Network
The work presents an automated method of segmentation of faces in color images with complex backgrounds. Segmentation of the face from the background in an image is performed by using face color feature information. Skin regions are determined by sampling the skin colors of the face in a Hue Saturation Value color model, and then training a fuzzy min-max neural network (FMMNN) to automatically segment these skin colors. The work appears to be the first application of Simpsons FMMNN algorithm to the problem of face segmentation. Results on several test cases showed recognition rates of both face and background pixels to be above 93%, except for the case of a small face embedded in a large background. Suggestions for dealing with this difficult case are proffered. The image pixel classifier is linear of order O(Nh) where N is the number of pixels in the image and h is the number of fuzzy hyperbox sets determined by training the FMMNN.3
Neuro-fuzzy Approach to Diagnosis of Leakages and Other Operational Faults in Water Distribution Networks
A lot of time, effort and resources is dedicated to purifying water but surprisingly high percentage of treated water is wasted through leakages in the water distribution networks (WDN) before even reaching customers. The occurrence of other operational faults like blocked pipes or erroneous states of valves etc. can also cause serious disruption in the services which need to be avoided. It is therefore very important that the state of the distribution system is continuously monitored. Unfortunately, due to financial constraints, it is not practical to measure all variables of interest and limited number of measurements is used together with WDN topology information to calculate the state of the WDN through state estimation procedures. Due to the scale of distribution systems an interpretation of the state can be quite a difficult task even for experienced system operators. Additionally if the so called topological error (e.g. leakage) occurs, the state estimation procedure usually results in a set of errors scattered across the network, making the diagnosis of the cause of the errors even more difficult. Though sequential analysis of precise numerical results of state estimation is useful, it also tends to ignore the greater picture of the overall system state which is something that experienced human operators primarily focus their attention on before analyzing the detail. Therefore a neuro-fuzzy approach, thought to be mimicking the information processing and abstraction forming by human operators, has been proposed as a solution to this problem.
A neuro-fuzzy pattern recognition approach to fault detection and identification based on the examination of patterns of state estimates has been proposed to solve the problem. A General Fuzzy Min-Max (GFMM) neural network for clustering and classification has been used as a main building block in the developed recognition system. This hyperbox fuzzy sets based method has been designed to be able to process inputs in a form of confidence (real value) intervals, learn on-line, grow to meet the demands of the problem and include new information without need for retraining of the whole system, and cope with labeled and unlabelled data reflecting the fact that some of the network states are known (i.e. normal operating state etc.) while others are not. To improve the efficiency of the learning process and adaptability, the diagnostic system has been designed as a two-level hierarchical system. The first level consists of a GFMM neural network which selects one of the n second level experts (implemented using GFMM NN as well). In terms of water distribution systems the purpose of the first level of this recognition system is to distinguish different typical behavior of the water system (i.e. night load, peak load etc.) while the second level components are responsible for the detection of anomalies for some characteristic load patterns. The second level can be, therefore, viewed as a collection of load-pattern-experts.4
Information taken from the following websites:
1. www.scifish.com
2. www.dcs.napier.ac.uk
3. www.worldscinet.com
4. www.eunite.org
