New Structural Evolving Algorithms For Fuzzy Systems

Recently, the issue of accuracy and interpretability trade-off has been getting more attention when designing new fuzzy systems. In this thesis, three evolving fuzzy models, namely enhancement of fuzzy term identification (EFTI), structure identification method (SIM) and structural evolving approach (SEA) are proposed to spot the best trade-off between accuracy and interpretability. EFTI, SIM and SEA are designed based on error reducing methods. EFTI is developed to fit with single input single output (SISO) problems (i.e. one dimension), while SIM and SEA are developed to fit with multi input single output (MISO) (medium and high dimension). EFTI begins with a simple fuzzy structure that is composed of two fuzzy terms in the input space. Then EFTI continues evolving by identifying splitting points of the input space that are compatible with the consequent parameters. On the other hand, SIM and SEA start with one fuzzy rule that has no fuzzy term in the input space regardless of the degree level of input dimension. Then they evolve on the basis of either closure or split processes for the selected input attribute of the selected subregion. If the selected attribute has no fuzzy terms, closure is performed, otherwise split is done. The evolving continues until a satisfactory accuracy is fulfilled or maximum number of subregion is reached. A partitioning technique based on the similarity feature and a static partition-selection technique are developed for SIM. While, a partitioning technique based on splitting the selected subregion into two subregions with maximum and minimum average error and a dynamic partition-selection technique are developed for SEA. Furthermore, a pruning technique based on the importance level of the fuzzy rules is proposed to shrink the rule-base of SEA. Compared with SISO models and using three datasets, EFTI produces the lowest RMSE with lowest number of rules. For MISO models and using nine benchmark datasets, SIM achieves the lowest RMSE with the smallest size of rule-base systems. Similarly, for MISO state-of-the-art models and using six benchmark datasets, SEA also produces the lowest RMSE with the smallest size of rule-base systems. In conclusion, the results proved that EFTI, SIM and SEA are able to produce a significant trade-off between accuracy and interpretabilit

Development and Numerical Optimization of a System of Integrated Agents for Serial Production Lines

In modern high-volume industries, the serial production line (SPL) is of growing importance due to the inexorable increase in the complexity of manufacturing systems and the associated production costs. Optimal decisions regarding buffer size and the selection of components when designing and implementing an SPL can be difficult, often requiring complex analytical models, which can be difficult to conceive and construct. Here, we propose a model to evaluate and optimize the design of an SPL, integrating numerical simulation with artificial intelligence (AI). Numerous studies relating to the design of SPL systems have been published, but few have considered the simultaneous consideration of a number of decision variables. Indeed, the authors have been unable to locate in the published literature even one work that integrated the selection of components with the optimization of buffer sizes into a single framework. In this research, a System of Integrated Agents Numerical Optimization (SIGN) is developed by which the SPL design can be optimized. A SIGN consists of a components selection system and a decision support system. A SIGN aids the selection of machine tools, buffer sizes, and robots via the integration of AI and simulations. Using a purpose-developed interface, a user inputs the appropriate SPL parameters and settings, selects the decision-making and optimization techniques to use, and then displays output results. It will be implemented in open-source software to broaden the impact of the SIGN and extend its influence in industry and academia. It is expected that the results of this research project will significantly influence open-source manufacturing system design and, consequently, industrial and economic development

A robust structure identification method for evolving fuzzy system

This paper proposes a robust structure identification method (RSIM) based on incremental partitioning learning. RSIM starts with an open region (initial domain) that covers all input samples. The initial region starts with one fuzzy rule without fuzzy terms and then evolves through incremental partitioning learning, which creates many subregions for system error minimization. The three major contributions of the proposed RSIM are as follows: It locates sufficient splitting points provided through a robust partitioning technique, determines the optimum trade-off between accuracy and complexity through a novel partition-selection technique, minimizes global error through global least square optimization. These contributions offer many remarkable advantages. First, RSIM provides a solution for the curse of dimensionality. Second, RSIM can also be applied to low-dimensional problems. Third, RSIM seeks to produce few rules with low number of conditions to improve system readability. Fourth, RSIM minimizes the number of fired rules. Therefore, RSIM can achieve low-level complexity systems. Three low-dimension and six high-dimension and real-life benchmarks are used to evaluate the performance of RSIM with state-of-the art methods. Although RSIM has high interpretability, the results prove that RSIM exhibits greater accuracy than other existing methods

Analysis of the problems of electricity in Iraq and recommendations of methods of overcoming them

The Iraqi power sector is at a critical stage that requires urgent reforms. Concerns regarding the ability of the system to provide steady power and its operational inefficiencies, in general, have been expressed by the industry and academia. Currently, the power industry is experiencing one of the highest demand growth in the world, and given that it is capital intensive, it takes up a large share of Iraq’s government capital investment program. More so, there is a huge increase in the financial burden of the Iraqi electricity supply industry due to the high subsidies required for the coverage of the recurrent expenditure. This subsidy is regarded as one of the highest in the world, given the extremely low traffic which does not cover up to 20% of the periodic expenditure. Apart from the huge subsidy in electricity tariff, another major and indirect socioeconomic subsidy is the fact that the industry is over-staffed with over 50,000 employees which under normal circumstances should not be more than 15,000. The estimated annual cost of the aforementioned inefficiencies and insufficient power supply is about $3 to 4 billion. In 2013, about 70% of the generated electricity was lost, and this loss is in three areas, including commercial, technical, and administrative losses. Therefore, there is a need for massive reforms that are targeted at addressing the entire issue. This can be achieved through the engagement of the private sector, higher competition, and the introduction of novel regulatory and legislative frameworks. In addition to that, the available sources of energy in Iraq need to be optimized alongside gas usage related to oil extraction, while solar energy in Iraq is explored and the solar hybridization of the current power stations

Enhancement of the efficiency of solar energy cells by selecting suitable places based on the simulation of PV System

At present, the increasing demand for electrical energy and the presence of renewable sources in various forms in the world and, particularly in Iraq, such as solar energy and wind energy, have become the focus of researchers' attention. Huge efforts are focused on finding ways to use ecologically friendly energy to generate electricity and eliminate fossil fuels. In this study paper, we propose the use of a simulation program to discover the ideal location for a solar cell and the amount of time to be exposed to the sun's rays, so that a home powered by solar energy can be built. Also, through this program, the losses were calculated that accompany the conversion of light energy into electrical energy to find the necessary solutions to make the solar cell work with high efficiency

Towards devising pilot experiments to establish parameter window for FSP of aluminum alloys

One of the major challenges encountered during friction stir processing (FSP) is the establishment of a process parameter window in order to achieve processed surfaces with an acceptable quality as it is an exhaustive task that involves enormous resources, time and efforts. Sometimes this task is so difficult that the trial may run into futility. This work belongs to a theme of FSP that is not much reported in the literature. This is a maiden work to lay a roadmap for the FSP parameter range in a quick and effective manner. The present study results from first-hand experiments performed to produce surface composites on AA6063 alloy using a mixture of SiC+Fe+Mn+Sn as reinforcement in such a manner that a novice professional can pan out ways to identify and classify irregularities/defects, associate them with the causes and obtain feasible parameter window. In this work, a methodology for identification and selection of optimum tool speed (rpm), processing speed and plunge depth has been demonstrated. The parameter window was established by analysing main surface irregularities associated with the parameters and taking corrective modification to eventually arrive at the feasible range. The established range was validated through an experiment performed with the parameters lying within the established window. The validation was supported with microstructural characterization, micro-hardness measurement, thermal analysis, corrosion analysis and the comprehensive analysis presented in this work has been done with the help of the image processing technique. Results show that grain refinement and homogeneous distribution of reinforcement present in the stir zone developed during FSP at the appropriate process parameters. Furthermore, grain refinement enhances the hardness by 28.29% and the corrosion resistance by 13.6%. The highest temperature i.e. 423.25°C is achieved on the advancing side of the processed zone

Learning Behavior of Memristor-Based Neuromorphic Circuits in the Presence of Radiation

In this paper, a feed-forward spiking neural network with memristive synapses is designed to learn a spatio-temporal pattern representing the 25-pixel character ‘B’ by separating correlated and uncorrelated afferents. The network uses spike-timing-dependent plasticity (STDP) learning behavior, which is implemented using biphasic neuron spikes. A TiO2 memristor non-linear drift model is used to simulate synaptic behavior in the neuromorphic circuit. The network uses a many-to-one topology with 25 pre-synaptic neurons (afferent) each connected to a memristive synapse and one post-synaptic neuron. The memristor model is modified to include the experimentally observed effect of state-altering radiation. During the learning process, irradiation of the memristors alters their conductance state, and the effect on circuit learning behavior is determined. Radiation is observed to generally increase the synaptic weight of the memristive devices, making the network connections more conductive and less stable. However, the network appears to relearn the pattern when radiation ceases but does take longer to resolve the correlation and pattern. Network recovery time is proportional to flux, intensity, and duration of the radiation. Further, at lower but continuous radiation exposure, (flux 1x1010 cm−2 s−1 and below), the circuit resolves the pattern successfully for up to 100 s

The Financial Fallout Unraveling Iraq’s Turmoil in the Wake of the U.S. Invasion

This report examines the financial losses and worsening conditions in Iraq caused by the U.S. war. The study will examine the immediate and long-term economic repercussions of the invasion. The introduction covers the U.S. invasion of Iraq. The event’s significance and Iraq’s post-invasion struggles are highlighted. The issue’s impacts on Iraq’s financial stability include infrastructural destruction, economic stagnation, and government financial constraints. This research will reveal how the U.S. invasion of Iraq has hurt the country’s economy and finances. Economic statistics and topic-related case studies are used. Iraq’s GDP has fallen, its unemployment rate has risen, its currency has depreciated, and its poverty rate has risen. Debt and a full public sector restrict the government from addressing basic needs. The third component of the research addresses the nation’s economic issues and makes solutions to speed up economic recovery. The ideas include reducing debt, reinvesting in infrastructure, seeking outside support, and establishing excellent governance and accountability. After the U.S. invasion of Iraq, the country’s economy has only worsened. Immediate action and international cooperation are needed to overcome these obstacles and give Iraqis a better future

The Detrimental Impact of Cash-Only Economy Iraq's Lag in Embracing Electronic Payment Methods

This report examines Iraq's economy and credit card ban on Retail's effects. This article addresses Iraq's late adoption of digital payment systems and the benefits of modernizing its payment infrastructure. Problem Statement Iraq's economy and banking system suffer without electronic payment options. This study examines Iraq's cash economy's effects on financial openness, tax revenue, consumer convenience, and GDP. This article examines Iraq's slow electronic payment progress. Comparing Iraq's digital payment system to others shows the need for improvement. This study used qualitative and quantitative methods. This study was underpinned an extensive literature review, in-depth interviews with key actors, and statistical data analysis from banks, governments, and international organizations. Three research approaches were used. Three figures demonstrate Iraq's cash-only economy's flaws. One study claims cash transactions hurt Iraq's economy. A regulatory vacuum causes money laundering, corruption, and government failure to collect taxes and other revenue. E-commerce and forward-thinking management have lagged without alternative electronic payment choices. This study examines Iraqi payment alternatives. Financial literacy efforts, public-private collaborations, and financial institution-technology supplier alliances can accelerate the cashless society transition. The data suggests Iraq should quickly switch to electronic payments. Economic development, financial inclusion, and openness will rise if the government succeeds

Artificial Intelligence’s Moderating Impact on the Relationship between Ethical Commitment and Creative Accounting Practices

AI moderates ethical commitment and creative accounting in this study. The goal is to examine how AI affects organisations’ ethical commitment and creative accounting practices. The mixed-methods study uses qualitative and quantitative data. The introduction discusses ethical accounting and creative accounting difficulties. It emphasises the necessity for good financial reporting ethics detection and prevention. The problem statement concerns the absence of study on how AI mitigates innovative accounting practices and affects ethical commitment. AI can improve accounting transparency, accuracy, and consistency. This study examines how AI moderates ethical commitment and creative accounting practices. It explores how AI can detect and avoid creative accounting, promoting ethical financial reporting. The methodology includes research design, participant selection, and data collection. It analyses financial data from a sample of organisations in various industries and interviews accounting professionals. The study results are in the results section. AI moderates the link between ethical commitment and inventive accounting practices. The results show that AI can reduce creative accounting and promote ethical financial reporting. The study advises organisations and policymakers. It recommends using AI-powered tools to detect and prevent creative accounting. Training and awareness programs are also stressed. Finally, this study examines AI’s moderating effect on ethical commitment and creative accounting practices. It shows how AI may reduce unethical behaviour and improve financial reporting ethics. AI in accounting and ethical decision-making should be studied in the future