RU-2861331-C1 - METHOD FOR ASSESSING BREEDING VALUE OF ANIMALS USING FUZZY SET-BASED BIT PHASE METHOD

Abstract

FIELD: agriculture. SUBSTANCE: computer-implemented method is proposed for the formation and evaluation of indicators of the breeding value of animals, including determining a list of phenotypic traits, taking into account the sex of the animal and assigning priority to each attribute. According to the invention, each phenotypic trait is associated with an affiliation function and a corresponding fuzzy set of values, while one or more of the following are used as affiliation functions: triangular, trapezoidal, Gaussian, Sigmoidal, Z-shaped or S-shaped; perform phasification of each phenotypic trait based on the specified membership functions; convert phasified values into a binary representation by assigning each value from a fuzzy set of a unique binary code with a variable bit slot length depending on the dimensionality of the fuzzy set; the binary codes of all features are combined into a single fuzzy bit vector arranged in descending order of feature priorities; the binary code of the animal category is additionally placed in the highest slot of the fuzzy bit vector; the resulting fuzzy bit vector is encoded using positional Base-encoding to obtain a compact SFI character index, which is used for comparing, ranking, and evaluating the breeding value of animals. EFFECT: invention makes it possible to flexibly analyze the phenotypic characteristics of animals without strict threshold values, compactly present information about the characteristics with the possibility of efficient data storage and transmission, and simplify the process of comparing animals. 1 cl, 1 tbl

Inventors

• Stukalin Aleksej Vadimovich

Dates

Publication Date

20260505

Application Date

20250221

Claims (1)

1. A computer-implemented method for forming and evaluating the indicators of breeding value of animals, including determining a list of phenotypic traits taking into account the sex of the animal and assigning a priority to each trait, characterized in that each phenotypic trait is associated with a membership function and a corresponding fuzzy set of values, wherein one or more of the following are used as membership functions: triangular, trapezoidal, Gaussian, sigmoidal, Z-shaped or S-shaped; each phenotypic trait is phasified based on the said membership functions; the phasified values are transformed into a binary representation by assigning to each value from the fuzzy set a unique binary code with a variable bit slot length depending on the dimension of the fuzzy set; the binary codes of all traits are combined into a single fuzzy bit vector, arranged in descending order of trait priorities; the binary code of the animal category is additionally placed in the highest slot of the fuzzy bit vector; The resulting fuzzy bit vector is encoded using positional Base coding to produce a compact symbolic index SFI, which is used for comparison, ranking, and assessment of the breeding value of animals.

Description

Introduction Assessing the breeding value of animals is an important task in modern livestock farming. An appropriate strategy for selecting breeding animals can improve herd productivity and genetic health, leading to increased economic efficiency. However, traditional methods for assessing breeding value are limited by strict statistical approaches that do not always account for the complexity and variability of phenotypic characteristics. In modern livestock farming, the Total Performance Index (TPI) and Net Merit Dollars (NM$) indices are widely used. These indices are based on the analysis of pedigree data, phenotypic traits, and the economic significance of characteristics. These indices allow for comparative analysis of animals within a population; however, they have several drawbacks, such as fixed weighting coefficients, strict thresholds, and a lack of flexibility in interpreting animal characteristics. This paper introduces a new method for analyzing fuzzy data: Fuzzy Bit Encoding. This method enables the transformation of fuzzy sets into a binary representation, ensuring compact storage, ease of mathematical analysis, and the ability to quickly compare fuzzy features. At the same time, a practical application of the bit fuzzification method for assessing the breeding value of animals is proposed. This approach is based on the Stukalin Fuzzy Index (SFI), which is based on the bit fuzzification method. These data form fuzzy bit vectors (Fuzzy Bit Vector) 1 (1. Fuzzy Bit Vector is a bit representation of fuzzy data, in particular fuzzy phenotypic traits. It is an ordered set of fuzzy bit slots, in which the slot position reflects the significance (weight) of the corresponding trait. The properties of this profile are discussed in detail in the article.), consisting of fuzzy bit slots (Fuzzy Bit Slot) 2 (2. Fuzzy Bit Slot is a binary representation of one fuzzy-defined trait as part of a fuzzy bit vector. The slot length is determined by the dimension of the fuzzy set, which determines the number of possible states of the trait. Thus, the proposed bit fuzzification method is not only a tool for analyzing the breeding value of animals, but also a universal way of processing fuzzy data in binary form. The use of fuzzy bit slots and fuzzy bit vector allows: • Flexibly analyze phenotypic characteristics of animals without strict threshold values. • Compactly represent information about features with the ability to efficiently store and transmit data. • Simplify the process of comparing animals using bit coding, where each fuzzy bit vector can be interpreted as a numerical value. • Adapt the index to the needs of a specific farm, tribe, region, breed or even country, allowing for local selection features and the economic significance of characteristics to be taken into account. This article also introduces new concepts for fuzzy set theory, such as the Fuzzified Bit Slot and Fuzzy Bit Vector, which formalize the process of binary encoding of fuzzy data. These concepts open up new possibilities for processing fuzzy data, simplifying their analysis and comparison using bitwise operations. The proposed method for calculating the index allows for a more accurate and adaptive assessment of the breeding value of an animal, ensuring convenient interpretation of the results and their application in breeding programs 3. (3. A breeding program is a set of scientifically based measures aimed at improving the genetic characteristics of animals, increasing productivity and ensuring sustainable breeding of the population in accordance with the goals of a specific farm, region or country. An additional advantage is the possibility of using this method not only in assessing the breeding value of animals, but also in other areas of data analysis that require working with fuzzy sets and binary coding. Fuzzy bit slot and fuzzy bit vectors To further describe the method, new concepts for the theory of fuzzy sets are introduced: fuzzy bit slot and fuzzy bit vector. A fuzzy bit vector (hereinafter referred to as a bit vector) is a bit representation of fuzzy data, particularly fuzzy phenotypic traits. It is an ordered set of fuzzy bit slots, in which the slot position reflects the significance (weight) of the corresponding trait. A fuzzy bit slot (hereinafter referred to as a bit slot) is a binary representation of a single fuzzy-defined feature within a bit vector. The slot's length is determined by the dimensionality of the fuzzy set, which determines the number of possible states for the feature. Properties of fuzzy bit vector and fuzzy ordering by feature importance. Each fuzzy bit slot represents a fuzzy value from a fuzzy set, and its weight is determined by the set high-order bits - the higher the bits, the more significant the feature they encode. Within a fuzzy bit vector, each slot also has a positional weight, depending on its position in the vector. The higher the slot (the further to the left), the more important