基于L-M神经网络优化算法的池塘水色判别系统的初步建立

为了将水产养殖水色判别传统技术经验转化为可以量化的数字技术,采用基于L-M神经网络优化算法和计算机图像处理技术的方法,建立了一个水色判别的水产养殖专家系统。通过实例预测,该系统判别误差率1%。该系统训练后的神经网络模型,能实现对养殖池塘水质的预测。系统的开发和使用对实现水产健康养殖、智能控制和计算机管理具有一定实用价值.     

基于模糊神经网络的池塘溶解氧预测模型

在分析了池塘溶解氧影响因素的基础上，利用模糊神经网络良好的非线性逼近能力建立了池塘溶解氧的模糊神经网络预测模型。神经网络模型如采用常规的BP或其它梯度算法，常导致训练时间较长且易陷入局部极小点，本实验采用快速的粒子群优化算法对模糊神经网络进行训练，收敛速度明显加快。实验结果表明采用该方法预报溶解氧的预测精度较常规BP递推算法的预测精度明显提高，所采用的模型能对溶解氧进行可靠的预测，该方法为研制开发智能水质检测仪以及工厂化养殖工作奠定了基础，对实际生产具有一定的指导意义。     

“湖泊渔业生态开发分类系统研究”课题通过专家鉴定

“湖泊渔业生态开发分类系统研究”课题通过专家鉴定由农业部立项的重点科研项目──“湖泊渔业生态开发分类系统研究”课题，于１９９４年１１月１９日在上海通过专家鉴定。专家认为该课题根据湖泊渔业发展需要，对湖泊进行生态开发分类系统的应用基础研究，选题正确；课...     

基于BP神经网络的养殖水质监控系统

基于BP神经网络的水产养殖水质监控系统以多个可编程控制器PLC和单片机系统作为下位机，检测现场数据；并用工业控制计算机实现现场监控和远程监控。软件方面，在现场监控计算机和远程计算机上设计了功能丰富的监控软件；并应用无线通信GPRS技术实现数据的传输。该系统对养殖水体溶解氧含量进行了自动监测和控制，性能稳定。     

基于遗传BP神经网络的半滑舌鳎体重估算模型优化研究

针对人工估算半滑舌鳎(Cynoglossus semilaevis)鱼苗体重存在误差大、难度大、易伤鱼苗等缺点,提出了一种基于遗传BP神经网络体重估算模型优化研究的方法。首先利用参考系测量鱼苗体长和体宽,再通过遗传BP神经网络估算模型计算鱼苗体重。利用遗传算法对编码后的BP神经网络进行优化并验证了遗传算法能有效确定BP神经网络隐藏层节点数目。结合遗传算法优化BP神经网络的结构和连接权值,采用300份同一训练样本对优化的BP神经网络进行训练,最终建立准确遗传BP神经网络体重估算模型。结果显示,该方法对鱼苗体重估算与实际值平均相对误差不超过0.61%。研究表明,该方法为半滑舌鳎体重估算提供了一种比较科学的计算方法,在鱼苗生长发育监测和科学喂养等方面具有重要的实际意义。     

BP神经网络模型在水环境质量综合评价应用中的一些问题

BP神经网络是采用误差反向传播算法对网络权值进行训练的多层前向神经网络，以其优良的非线性逼近能力，获得广泛应用，建立的神经网络模型具有优异性能的必要条件是神经网络结构及其参数的合理选取，具有足够多和代表性，典型性好的训练样本，训练时求得全局最小点和不出现“过学习”或“过拟合”等，本文根据近几年BP神经网络建模理论研究的最新成果，研究发现目前在建立水环境质量综合评价BP神经网络模型时存在的几个主要问题：（1）训练样本太小；（2）没有检验样本和测试样本；（3）神经网络结构太大等，从而极有可能造成在训练神经网络模型时再现“过拟合”或“过学习”现象，使建立的模型泛化能力较差或根本没有，在研究近年提出的应用BP神经网络方法建模的必备条件的基础上，对目前应用人工神经网络进行水环境质量综合评价的研究成果的分析表明，绝大多数水环境质量BP神经网络评价模型是在满足建模条件的情况下建立的，计算实例表明，在不满足建模条件下建立的神经网络模型泛化能力和预测能力较差，极有可能出现多模式现象，没有实用价值。     

“鱼虾育苗室化学要素自动系统”研制成功

由国家海洋局海洋技术研究所承担的海洋局科技兴海项目——“鱼虾育苗室化学要素自动系统”日前通过了国家海洋局组织的鉴定。专家认为该项成果填补了国内空白，达到了国际先进水平。　　该系统是由水下传感器与计算机、打印平台组成的水质自动装置，在无人职守的情况下，系统可自动、连续完成鱼虾及海珍品养殖水质环境的长期监测。目前主要提供的养殖海水环境实时要素为温度、pH值、溶解氧。使用该系统将大大提高海珍品亲体饲养、孵化、育苗期的科学性，节约成本，提高产量，增加经济效益。　　“鱼虾育苗室化学要素自动系统”还可广泛…     

海水活鱼运输装置自动监控系统的设计

以AT89C51单片机为核心设计了一套适用于活鱼运输装置的自动监控系统，本文描述该系统的工作原理和系统的软硬件设计，实验结果表明，该系统设计方案合理，应用于活鱼运输具有较高的性价比，且结构简单、性能可靠、实用性强等特点。     

改革开放扩大出口权——给推广笼捕梭子蟹技术提供条件

笼捕梭子蟹技术，是我市水产专家和水产科技人员在1985年开始研究的，现已取得完整的技术资料，并通过了省科委组织的技术鉴定。该作业具有产量高、成本低、效益好的特点，耗油省，工具使用寿命长，操作方便，劳动强度低，暂养成活率高达95％以上。     

《福建水产》编辑部2011年网上投稿启事

《福建水产》在线编辑系统已正式运行,该系统基于Internet平台,实现作者——编辑——审稿专家——编委——定稿会专家——主编等六位一体的协作化、网络化、角色化的编辑稿件业务处理平台。实现从接收作者来稿到送审、退修改、编辑加工、录用等全流程管理；     

BP人工神经网络模型在珠江口水质评价中的应用

根据海水水质标准GB30971997,应用VB编程语言在各类海水水质指标浓度区间内生成足够多的随机分布样本,以此作为海水水质评价BP人工神经网络模型的训练、检验和测试样本,利用训练后的海水水质评价BP人工神经网络模型对珠江口2002～2003年水质状况做出评价。结果表明,训练后的海水水质评价BP人工神经网络模型具有较好的泛化能力,能够准确评价未知海水样本的水质类别;2002～2003年珠江口的水环境总体状况较差,绝大部分区域属于II～IV类海水,在其分布上,珠江口西部海域水质状况好于东部海域,这是由于珠江口东部沿岸城市东莞和深圳较大的排污量和繁忙的海上运输所引起。     

渔业资源评估专家系统设计及实践

利用国内外常用的渔业资源评估模型及东海主要经济种类渔业资源评估专家知识,应用面向对象的知识处理系统（ＯＫＰＳ）作为开发工具,开发了渔业资源评估专家系统。运用该专家系统可以对东海主要经济种类,如带鱼、鲐鱼和马丽等的资源量、可捕量进行评估和预报。     

The skipjack tuna fishery in the west-central Pacific Ocean: applying neural networks to detect habitat preferences

Spatial models for habitat selection were developed using neural networks. The model specifications were elucidated from model construction, training, validating, testing, and interpretation, and applied to skipjack tuna in the west-central Pacific Ocean. The model was created using commercial data from the Oceanic Fisheries Programme of the South Pacific Fisheries Commission and oceanic environmental data include sea surface temperature, horizontal gradient of sea surface temperature calculated from sea surface temperature, sea surface height, and chlorophyll-a. Local abundance indices for skipjack tuna were compiled using catch per unit effort, catch or effort. The optimal neural network models for each abundance index were selected by mean square errors and average relative variances. The predictive ability for optimal neural network models was evaluated by the R ² value using a cross-validation approach. The accuracy and stability of the optimal models, the contribution of independent variables, and the distribution of spatial sensitivity analyses were shown to vary with the abundance index chosen as the response variable. Chlorophyll-a was the most significant oceanographic factor in habitat selection. These results improve our understanding of how best to apply neural networks for modeling habitat selection by skipjack tuna.     

Comparison between traditional methods and artificial neural networks for ammonia concentration forecasting in an eel (Anguilla anguilla L.) intensive rearing system

One of the main problems in the management of fishfarms with water recirculating system is the forecasting and control of ammonia concentration in order to minimise the fish stress status. This paper examines methodologies of prediction in a real-time environment for an eel intensive rearing system. Approaches based on linear multiple regression, univariate time series models (exponential smoothing and autoregressive integrated moving average (ARIMA) models) and computational neural networks (ANNs) are developed to predict the daily average ammonia concentration in rearing tanks with water recirculating. The models are established using actual data from an eel fishfarm in southern Spain. The input variables used in the models (multiple regression, Holt smoothing model, ARIMA models and ANN models) are the ammonia concentration of previous days. In ANN models, the training method used is a standard back-propagation variation known as extended-delta-bar-delta (EDBD). Different neural architectures, whose learning is carried out by crossvalidation and controlling several threshold determination coefficients, are compared. Globally, the nonlinear ANN model approach is shown to provide a better prediction of daily average ammonia concentration than linear multiple regression and univariate time series analysis when the correlation between data series is low and when the models were obligated to predict in a situation for which specifically had not been calibrated. The best results were obtained by 5:10s:15s:1l ANN model in the pre-growth series.     

Looking beyond the horizon: An early warning system to keep marine mammal information relevant for conservation

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Prediction of shrimp growth using an artificial neural network and regression models

Uncertainty and lack of information about the future make it difficult for shrimp farmers to develop and plan harvesting schedules. To do this effectively, farmers should be able to predict shrimp growth. A reliable prediction of growth and survival would also give farmers a better insight into future productivity and profitability. Linear and nonlinear regression models have been used to estimate growth of different types of animals. These models include theoretic guesses and hypotheses about the underlying laws that govern the system from which data are generated. Compared to such models, artificial neural networks (ANNs) make a few priori assumptions about the models and suited for predicting animal growth. This study evaluated the potential of an ANN as an alternative to regressions models for predicting shrimp growth. Empirical data were collected from 9 commercial shrimp farms in the Bushehr Province of Iran. The results showed that the ANN performed better compared to linear and nonlinear regression models for predicting the growth of farmed shrimp.     

The use of information technology in aquaculture management

Abstract

The recent advances in information technology (IT) have had profound impacts on all walks of life and aquaculture is no exception. The growing importance of aquaculture as an alternative source of protein has further emphasized the need to adapt and develop advanced IT for the better management of aquaculture facilities as well as the regional planning for aquaculture development.

It is the objective of this paper to review the use and potential prospects of IT in aquaculture management. The information technologies considered are instrumentation and process control, data management, computerized models, decision support systems, artificial intelligence and expert systems, image processing and pattern recognition, geographical information systems, and information centres and networks. The review includes a brief introduction of each of the aforementioned technologies, followed by a survey of their current application as well as their potential use in aquaculture management.     

Fish‐Net: Probabilistic models for fishway planning,design and monitoring to support environmentally sustainable hydropower

The construction of fishways for upstream and downstream connectivity is the preferred mitigation measure for hydropower dams and other riverine barriers. Yet empirical evidence for effective design criteria for many species is missing. We therefore assembled a group of international fishway designers and combined their knowledge with available empirical data using a formal expert elicitation protocol and Bayesian networks. The expert elicitation method we use minimizes biases typically associated with such approaches. Demonstrating our application with a case‐study on the temperate Southern Hemisphere, we use the resulting probabilistic models to predict the following, given alternative design parameters: (i) the effectiveness of technical fishways for upstream movement of migratory fish; (ii) habitat quality in nature‐like bypasses for resident fish; and (iii) rates of mortality during downstream passage of all fish through turbines and spillways. The Fish Passage Network (Fish‐Net) predicts that fishways for native species could be near 0% or near 100% efficient depending on their design, suggesting great scope for adequate mitigation. Sensitivity analyses revealed the most important parameters as follows: (i) design of attraction and entrance features of technical fishways for upstream migration; (ii) habitat preferences of resident fish in nature‐like bypasses; and (iii) susceptibility of fish to barotrauma and blade strike during turbine passage. Numerical modelling predicted that mortality rates of small‐bodied fish (50–100 mm TL) due to blade strike may be higher for Kaplan than Francis turbines. Our findings can be used to support environmentally sustainable decisions in the planning, design and monitoring stages of hydropower development.