COMPARATIVE ANALYSIS OF ERRORS IN NEURAL NETWORKS OF ARTIFICIAL INTELLIGENCE
Keywords:
Neural Networks, Artificial Intelligence, Error Analysis, Training Errors, Generalization, Model Optimization, Error Detection, AI Performance.Abstract
This paper presents a comparative analysis of errors in neural networks used in artificial intelligence (AI) systems. Neural networks are central to many AI applications, but their performance is often influenced by various types of errors that can affect their accuracy and reliability. The study investigates the different categories of errors, including training errors, validation errors, and generalization errors, and examines the impact of these errors on the overall performance of AI models. We compare different approaches to error detection, correction, and minimization, highlighting the challenges and potential solutions for enhancing the robustnes of
neural networks. The findings aim to provide insights into improving error handling and optimization strategies in AI systems, thereby contributing to the development of more reliable and efficient AI models.
References
1.
Ergashev, O. M., Turgunov, B. X., & Turgunova, N. M. (2023). Microprocessor Control
System for Heat Treatment of Reinforced Concrete Products. INTERNATIONAL JOURNAL OF
INCLUSIVE AND SUSTAINABLE EDUCATION, 2(5), 11-15.
2.
Ergashev, O. M., & Ergasheva, S. M. (2023). FOYDALANUVCHI INTERFEYSLARIDA
MULTIMEDIA IMKONIYATLARI, AXBOROT NAMOYISH ETISH
SHAKLLARI. International Journal of Contemporary Scientific and Technical Research, 179-181.
3.
Ergashev, O. M., & Turgunov, B. X. (2023). INTELLIGENT OPTOELECTRONIC
DEVICES FOR MONITORING AND RECORDING MOVEMENT BASED ON HOLLOW
FIBERS. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER
SCIENCES, 4(5), 34-38.
4.
Mirzapolatovich, E. O., Eralievich, T. A., & Mavlonzhonovich, M. M. (2022). Analysis of
Static Characteristics Optoelectronic Level Converters Liquids and Gases Based on Hollow Light
Guides. European journal of innovation in nonformal education, 2(6), 29-31.5.
Mirzapo‘lotovich, E. O., & Mirzaolimovich, S. M. (2022). TA’LIMDA JARAYONIDA
LMS TIZIMLAR TAXLILI. TA'LIM VA RIVOJLANISH TAHLILI ONLAYN ILMIY JURNALI, 118
122.
6.
Sobirovich, K. V., Mirzapulotovich, E. O., & Mirzaolimovich, S. M. (2023). Advantages of
using LMS as a System for Monitoring, Evaluating and Monitoring Learning Outcomes.
7.
Shipulin, Y. G., Raimzhonova, O. S., Ergashev, O. M., & Usmanov, Z. K. (2021). Method
for Ensuring Continuous Functioning of Multichannel Systems for Control and Recording of Water
Composition in Seismic Wells.
8.
Mirzapulatovich, E. O., Eralievich, T. A., & Mavlonjonovich, M. M. (2022).
MATHEMATICAL MODEL OF INCREASING THE RELIABILITY OF PRIMARY
MEASUREMENT INFORMATION IN INFORMATION-CONTROL SYSTEMS. Galaxy
International Interdisciplinary Research Journal, 10(5), 753-755.
9.
Ergashev, O. (2023). ANDROID PLATFORMASIGA DASTUR TA’MINOT YOZISHDA
DASTURLASH TILINI TANLASH AHAMIYATI. Engineering problems and innovations.
10.
Ergashev, O. (2023). ELEKTRON HUJJAT ALMASHINUVINING O’ZIGA XOS
HUSUSIYATLARI. Engineering problems and innovations.
11. Ergashev, O. (2023). AXBOROT TEXNOLOGIYALARIGA OID QONUN VA
QARORLARNING INFORMATIKA VA AXBOROT TEXNOLOGIYALARI FANINI
O‘QITISHDAGI AHAMIYATI. Engineering problems and innovations.
