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FRBSIFC: Fuzzy Rule Based System for Iris Flower Classification
Post: #1

.doc  Fuzzy rule based system for iris flower classification by labin senapati.doc (Size: 856 KB / Downloads: 59)
Information technology (IT) doubtlessly contributes much to agriculture and rural development. This article investigate the use of FRBSIFC (Fuzzy Rule Based system for Iris Flower Classification) applied to discovery of Logical Rules in order to classify the Iris flower. FRBS is thought to be a useful paradigm for the implementation of human knowledge; this provides a means for sharing, communicating and transferring the human knowledge. FRBSIFC (Fuzzy Rule Based system for Iris Flower Classification) Is described in some detail, then an evaluative study is undertaken involving the subjective evaluation of the results. FRBSIFC is found to discover logical rules for the classification of Iris Flower.
Keywords: FRBSIFC (Fuzzy Rule Based system for Iris Flower Classification), FRBS, logical rules
1. Introduction
Information technology (IT) doubtlessly contributes much to agriculture and rural development. Firstly, it can facilitate rural activities and provide more comfortable and safe rural life with equivalent services to those in the urban areas, such as provision of distance education, tele-medicine, remote public services, remote entertainment etc. Secondly, IT can initiate new agricultural and rural business such as e-commerce, real estate business for satellite offices, rural tourism, and virtual corporation of small-scale farms. Thirdly, it can support policy-making and evaluation on optimal farm production, disaster management, agro-environmental resource management etc., using tools such as geographic information systems (GIS). Fourthly, it can improve farm management and farming technologies by efficient farm management, risk management, effective information or knowledge transfer etc., realizing competitive and sustainable farming with safe products. For example, farmers must make critical decisions such as what to and when to plant, and how to manage pests, while considering off-farm factors such as environmental impacts, market access, and industry standards. IT-based decision support system (DSS) can surely help their decisions. Fifthly, IT can provide systems and tools to secure food traceability and reliability that has been an emerging issue concerning farm products since serious contamination such as BSE and chicken flu was detected.
Finally, IT can take an important and key role for industrialization of farming or farm business enterprises, combining the above roles.
FRBSIFC (Fuzzy Rule Based system for Iris Flower Classification) is applied to discovery of Logical Rules in order to classify the Iris flower. FRBS is thought to be a useful paradigm for the implementation of human knowledge; this provides a means for sharing, communicating and transferring the human knowledge. Here the iris flower are classified into three classes namely:
 Iris setosa.
 Iris versicolor.
 Iris virginica.
All these classes has the common physical characteristic, they are
• Sepal length
• Sepal width
• Petal length
• Petal width
where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.
Fuzzy Rule based system can be applied to define a set of rules which can classify the flower by reading its physical characteristics. FRBSIFS is a type of machine learning technique, which will read the previously recorded data about the Iris flower and then generates optimal logical rules and when the unknown samples are introduced to these rules, it will identify the samples accurately.
Iris is a genus of 260 species of flowering plants with showy flowers. It takes its name from the Greek word for a rainbow, referring to the wide variety of flower colors found among the many species. As well as being the scientific name, iris is also very widely used as a common name; for one thing, it refers to all Iris species, though some plants called thus belong to other closely related genera. In North America, a common name for irises is 'flags', while the plants of the subgenus Scorpiris are widely known as 'junos', particularly in horticulture. It is a popular garden flower in the United States.
The iris flower is of special interest as an example of the relation between flowering plants and pollinating insects. The shape of the flower and the position of the pollen-receiving and stigmatic surfaces on the outer petals form a landing-stage for a flying insect, which in probing the perianth for nectar, will first come in contact of perianth, then with the stigmatic stamens in one whorled surface which is borne on an ovary formed of three carpels. The shelf-like transverse projection on the inner whorled underside of the stamens is beneath the over-arching style arm below the stigma, so that the insect comes in contact with its pollen-covered surface only after passing the stigma; in backing out of the flower it will come in contact only with the non-receptive lower face of the stigma. Thus, an insect bearing pollen from one flower will, in entering a second, deposit the pollen on the stigma; in backing out of a flower, the pollen which it bears will not be rubbed off on the stigma of the same flower.
The iris fruit is a capsule which opens up in three parts to reveal the numerous seeds within. In some species, these bear an aril. In water purification, Yellow Iris (I. pseudacorus) is used. The roots are usually planted in a substrate (e.g. lava-stone) in a reedbed-setup. The roots then improve water quality by consuming nutrient pollutants, such as from agricultural runoff.
Iris setosa are vigorous plants with strong, sword-like foliage about 2 ft. in height. The iris flowers are purple-blue, usually of a very dark shade, but occasionally pale lavender and intermediate shades. Flowers are 3-6 in. wide.
A member of the iris family (family Iridaceae) which consists of herbs growing from rhizomes, bulbs, or corms, with narrow basal leaves and showy clusters at the tips of long stalks. There are about 60 genera and 1,500 species, distributed in temperate and tropical regions. Among them, Iris, Freesia, Gladiolus, Bugle Lily, and Montbretia are popular ornamentals. Saffron dye is obtained from Crocus, and essence of violets, used in perfumes, is extracted from the rhizomes of Iris.
Iris versicolor contain notable amounts of terpenes, and organic acids such as ascorbic acid, myristic acid, tridecylenic acid and undecylenic acid. Iris rhizomes can be toxic. Larger Blue Flag (I. versicolor) and other species often grown in gardens and widely hybridized contain elevated amounts of the toxic glycoside iridin. These rhizomes can cause nausea, vomiting, diarrhea, and/or skin irritation, but poisonings are not normally fatal. Irises should only be used medicinally under professional guidance.
Iris virginica, Blue Flag Iris is a tall, bold wildflower with pale-green sword-like leaves in strong, flat vertical fans. The showy flowers are 3.5 inches wide, and deep blue-violet with yellow and white markings growing on stems up to 3 feet tall. It will grow in ordinary garden soil but prefers moist, rich soil where it forms colonies and can be used in bog or water gardens. The foliage is strongest when planted in partial shade but the flowers bloom best in full sun and can be used in flower arrangements. Native blue flag iris is an emergent wildflower. The root mass of established colonies provides good shoreline protection. Iris is Greek for "rainbow". Seeds should be planted in fall/winter or receive 3 months cool, moist stratification.
Fuzzy logic has emerged as a more general form of logic that can handle the concept of partial truth. Truth here takes intermediate values between "completely true" and "completely false". Since the pioneering work of Zadeh (1965), fuzzy logic has been used as a modeling methodology that allows easier transition between humans and computers for decision making and a better way to handle imprecise and uncertain information. This methodology has undergone several developments and is currently widely used in machine control.
One area where fuzzy systems have been applied is multi-objective decision making with imprecise objectives, such as multi-objective reservoir operation. Fontane et al. (1997) posed this problem using linguistically described operational goals and constraints with fuzzy membership functions. Their study included conducting interviews of both decision makers and representatives of decision-influence groups to develop measures of multiple fuzzy objectives as membership functions in terms of reservoir release or storage. Multiple objectives were treated as constraints, and the end-of-the-year storage as a goal.
In the area of replicating complex mathematical models, Bârdossy et al. (1995) modeled the movement of water in the unsaturated zone using a fuzzy rule-based approach. Data generated by numerical solution of Richard's equation were used as examples to train (i.e. formulate the rules of) a fuzzy rule-based model.
Bârdossy & Duckstein (1995) also used adaptive fuzzy systems to model daily water demand time series in the Ruhr basin, Germany, and used fuzzy rules to predict future water demand based on three input variables: the day of the week, the daily maximum temperature, and the general weather conditions of the previous days. In the area of classification, the study by Carpa et al. (1994) showed that the theory of fuzzy sets can be applied to drought classification. They used fuzzy clustering techniques to identify areas with similar and homogenous meteorological characteristics.
The major problem in using fuzzy rule-based modeling is the formulation of rules. In cases of simpler systems, the fuzzy rules could be obtained from expert knowledge. In more complex systems, however, all the rules cannot be manually formulated (since such rules lack numerical precision) and it is vital to use intelligent systems that can configure their own working rules. A computer-based tool with such capabilities is developed herein, tested with data generated using hypothetical models of varying nonlinearity in which promising results were obtained, and applied to the problem in this research.
A fuzzy rule-based model contains membership functions of fuzzy sets constructed on the range of all the inputs to the model. The membership functions could be represented by linguistic terms like "low", "medium" and "high". The output also contains membership functions. The model matches the input and output with fuzzy rules such as:
If Input 1 is LOW and Input 2 is HIGH then Output is MEDIUM
Since membership functions overlap each other, so do the rules constructed from them.
Figure 1 illustrates a glimpse of what goes on in a fuzzy model, in a situation where there are two inputs and one output. When a vector of input data is fed into the model, membership values to the corresponding input fuzzy sets are determined.
For instance, x1 belongs to HIGH and MEDIUM while x2 belongs to LOW and MEDIUM.
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